Publications

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Hors d’oeuvres: new material, preprints, personal favourites. 

The complete list

The gourmand’s dinner complete: chronologically arranged.

By topic

A la carte for the discerning diner: arranged by subject matter.  Papers crossing boundaries may appear in multiple categories.

Open Books

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  1. V. Amelkin, S. S. Venkatesh, and R. Vohra, ``Contagion and Equilibria in Diversified Financial Networks,'' Econometrica, submitted November 2021.

  2. A. DasGupta, T. M. Sellke, and S. S. Venkatesh, ``The Exact and Asymptotic Distributions of X mod k and Connections to Tauberian Theorems, Congruence Algebra, and Fourier Analysis,'’ unpublished, 2021.

  3. S. S. Venkatesh, ``Report of the Chair of the Faculty Senate,’' The Almanac of the University of Pennsylvania: Supplements, vol. 64, issue 34, May 8, 2018.

  4. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,'' Online Social Networks and Media, vol. 5, pp. 1–22, March 2018.

  5. S. Eshghi, M. H. R. Khouzani, S. Sarkar, N. B. Shroff, and S. S. Venkatesh, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' IEEE Transactions on Automatic Control, vol. 60, no. 6, pp. 1554–1569, June 2015.

  6. S. Khanna, S. S. Venkatesh, O. Fatemieh, F. Khan, and C. A. Gunter, ``Adaptive Selective Verification: An Efficient Adaptive Countermeasure to Thwart DoS Attacks,''IEEE Transactions on Networking, vol. 20, issue 3, pp. 715–728, June 2012.

  7. Z. Liu, S. S. Venkatesh, and C. C. Maley, ``Sequence Space Coverage, Entropy of Genomes and the Potential to Detect Non-Human DNA in Human Samples,'' BMC Genomics, vol. 9, pp. 509–526, October 2008.

  8. S. S. Kunniyur and S. S. Venkatesh, ``Threshold Functions, Node Isolation, and Emergent Lacunae in Sensor Networks,' IEEE Transactions on Information Theory, vol.~52, no.~12, pp.~53525372, December 2006.

  9. S. S. Venkatesh, ``CDMA Capacity,'' 2000 Conference on Information Sciences and Systems, Princeton University, March 15-17, 2000.

  10. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' Random Structures and Algorithms, vol. 14, pp. 345-381, 1999.

  11. S. R. Kulkarni, G. Lugosi, and S. S. Venkatesh, ``Learning Pattern Classification—A Survey,' IEEE Transactions on Information Theory, Special Commemorative Issue 1948-1998, vol. 44, no. 6, pp. 2178-2206, 1998.

  12. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Expansions of the k-Nearest Neighbor Risk,'' Annals of Statistics, vol. 26, no. 3, pp. 850-878, 1998.

  13. A. Orlitsky and S. S. Venkatesh, ``On edge-colored interior planar graphs on a circle and the expected number of RNA secondary structures,'' Discrete Applied Mathematics, vol. 64, pp. 151-178, 1996.

  14. S. S. Venkatesh, ``On Approximations of Functions by Depth-Two Neural Networks,'' IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994; reprinted in Proceedings 1994 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1994.

  15. I. Hu and S. S. Venkatesh, ``On the Minimum Expected Duration of a Coin Tossing Game,'' IEEE Transactions on Information Theory, vol. 39, no. 2, pp. 581-593, 1993.

  16. S. S. Venkatesh, ``Directed Drift: A New Linear Threshold Algorithm for Learning Binary Weights On-Line,Journal of Computer and Systems Sciences, vol. 46, no. 2, pp. 198-217, 1993.

  17. S. S. Venkatesh, ``Robustness in Neural Computation: Random Graphs and Sparsity,'' IEEE Transactions on Information Theory, vol. 38, no. 3, pp. 1114-1118, 1992.

  18. S. S. Venkatesh and D. Psaltis, ``On Reliable Computation with Formal Neurons,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 87-91, 1992.

  19. S. S. Venkatesh and J. Franklin, ``How Much Information Can One Bit of Memory Retain About a Bernoulli Sequence?'' IEEE Transactions on Information Theory, vol. IT-37, pp. 1595-1604, 1991.

  20. S. S. Venkatesh and D. Psaltis, ``Linear and Logarithmic Capacities in Associative Neural Networks,'' IEEE Transactions on Information Theory, vol. IT-35, pp. 558-568, 1989.  CorrectionIEEE Transactions on Information Theory, vol. IT-53, p. 854, 2007.

  21. P. Baldi and S. S. Venkatesh, ``Number of Stable Points for Spin Glasses and Neural Networks of Higher Orders,'' Physical Review Letters, vol. 58, pp. 913-916, 1987.

  22. R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, ``The Capacity of the Hopfield Associative Memory,'' IEEE Transactions on Information Theory, vol. IT-33, pp. 461-482, 1987.
 

Publication chronology: abstracts & papers

  1. D. Psaltis, E. G. Paek, and S. S. Venkatesh, ``Acousto-Optic/CCD Image Processor,'' Proceedings of the International Optical Computing Conference, MIT, Cambridge, Massachusetts, pp. 204-208, 1983.

  2. D. Psaltis, S. S. Venkatesh, and E. G. Paek, ``Optical Image Correlation with a Binary Spatial Light Modulator,'' Optical Engineering, vol. 23, no. 6, pp. 698-704, 1984.

  3. S. S. Venkatesh, ``Epsilon Capacity of Neural Networks,'' Conference on Neural Networks for Computing, Snowbird, Utah, April 1986; reprinted in Neural Networks for Computing, (ed. J. Denker). New York: AIP, 1986.

  4. D. Psaltis, J. Hong, and S. S. Venkatesh, ``Shift-Invariance in Optical Associative Memories,'' in Proceedings of SPIE: First Annual Symposium on Optoelectronics and Laser Applications, Los Angeles, California, 1986.

  5. S. S. Venkatesh, ``Computation with Neural Networks,'' Proceedings of the Ninth Annual Conference on Engineering in Medicine and Biology, Boston, Massachusetts, 1987, pp. 1364-1365.

  6. R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, ``The Capacity of the Hopfield Associative Memory,'' IEEE Transactions on Information Theory, vol. IT-33, pp. 461-482, 1987.

  7. P. Baldi and S. S. Venkatesh, ``Number of Stable Points for Spin Glasses and Neural Networks of Higher Orders,'' Physical Review Letters, vol. 58, pp. 913-916, 1987.

  8. P. Baldi and S. S. Venkatesh, ``On Properties of Networks of Neuron-Like Elements,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1987; reprinted in Neural Information Processing Systems, (ed. D. Anderson). New York: AIP, 1988.

  9. S. S. Venkatesh, D. Psaltis, and G. Sirat, ``A Class of Spectral Algorithms for Neural Associative Memory,IEEE International Symposium on Information Theory, Kobe, Japan, June 1988.

  10. S. S. Venkatesh and D. Psaltis, ``A General Characterisation of Rotation-Invariant Image Classifiers,' IEEE International Symposium on Information Theory, Kobe, Japan, June 1988.

  11. D. Psaltis and S. S. Venkatesh, ``Information Storage in Fully Interconnected Networks,'' in Evolution, Learning, and Cognition, (ed. Y. C. Lee). Teaneck, New Jersey: World Scientific, 1989.

  12. S. S. Venkatesh and D. Psaltis, ``Binary Filters for Pattern Classification,'' IEEE Transactions on Acoustics, Speech, Signal Processing, vol. ASSP-37, pp. 604-611, 1989.

  13. S. S. Venkatesh and D. Psaltis, ``Linear and Logarithmic Capacities in Associative Neural Networks,'' IEEE Transactions on Information Theory, vol. IT-35, pp. 558-568, 1989.  CorrectionIEEE Transactions on Information Theory, vol. IT-53, p. 854, 2007.

  14. S. S. Venkatesh, ``What is the Capacity of One Bit of Memory?,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  15. S. S. Venkatesh, ``Computation and Learning in Neural Networks with Binary Weights,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  16. S. S. Venkatesh and D. Psaltis, ``Error Tolerance and Neural Capacity,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  17. S. S. Venkatesh, G. Pancha, D. Psaltis, and G. Sirat, ``Shaping Attraction Basins in Neural Networks,' Neural Networks, vol. 3, no. 6, pp. 613-624, 1990.

  18. S. S. Venkatesh and P. Baldi, ``Programmed Interactions in Higher-Order Neural Networks: Maximal Capacity,Journal of Complexity, vol. 7, no. 3, pp. 316-337, 1991.

  19. S. S. Venkatesh and J. Franklin, ``How Much Information Can One Bit of Memory Retain About a Bernoulli Sequence?'' IEEE Transactions on Information Theory, vol. IT-37, pp. 1595-1604, 1991.

  20. S. S. Venkatesh and P. Baldi, ``Programmed Interactions in Higher-Order Neural Networks: The Outer-Product Algorithm,'' Journal of Complexity, vol. 7, no. 4, pp. 443-479, 1991.

  21. R. Snapp, D. Psaltis, and S. S. Venkatesh, ``Asymptotic Slowing Down of the Nearest Neighbour Classifier,Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman), San Mateo, California: Morgan Kaufmann, 1991.

  22. S. Biswas and S. S. Venkatesh, ``The Devil and the Network: What Sparsity Implies to Robustness and Memory,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman). San Mateo, California: Morgan Kaufmann, 1991.

  23. S. S. Venkatesh, ``On Learning Binary Weights for Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, August 1991; reprinted in Proceedings of the Fourth Workshop on Computational Learning Theory, (eds. L. G. Valiant and M. K. Warmuth). San Mateo, California: Morgan Kaufmann, 1991.

  24. S. S. Venkatesh, R. Snapp, and D. Psaltis, ``Bellman Strikes Again—The Rate of Growth of Sample Complexity with Dimension for the Nearest Neighbour Classifier,' Workshop on Computational Learning Theory, University of Pittsburgh, Pittsburgh, Pennsylvania, July 1992; reprinted in Proceedings of the Fifth Workshop on Computational Learning Theory, Baltimore, Maryland: ACM Press, 1992.

  25. J. Ratsaby and S. S. Venkatesh, ``Learning with Few Labelled Examples,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1992.

  26. S. S. Venkatesh, ``Computation and Learning in the Context of Neural Network Capacity,'' in Neural Networks for Perception, (ed. H. Wechsler). New York: Academic Press, 1992.

  27. S. S. Venkatesh and D. Psaltis, ``On Reliable Computation with Formal Neurons,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 87-91, 1992.

  28. S. S. Venkatesh, ``The Science of Making Erors,'' IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 135-144, 1992.

  29. S. S. Venkatesh, ``Robustness in Neural Computation: Random Graphs and Sparsity,'' IEEE Transactions on Information Theory, vol. 38, no. 3, pp. 1114-1118, 1992.

  30. I. Hu and S. S. Venkatesh, ``On the Minimum Expected Duration of a Coin Tossing Game,'' IEEE International Symposium on Information Theory, San Antonio, Texas, January 1993; reprinted in Proceedings 1993 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1993.

  31. D. Psaltis, R. Snapp, and S. S. Venkatesh, ``On the Finite Sample Performance of the Nearest Neighbour Classifier,IEEE International Symposium on Information Theory, San Antonio, Texas, January 1993; reprinted in Proceedings 1993 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1993.

  32. S. C. Fang and S. S. Venkatesh, ``On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalisation in Learning Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1993; reprinted in Proceedings of the Sixth Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1993.

  33. B. Sarath and S. S. Venkatesh, ``Auditor Liability for Management Fraud,'' Fifth Annual Conference on Intelligent Systems in Accounting, Finance, and Management, Palo Alto, California, November 1993.

  34. C. Wang, S. J. Judd, and S. S. Venkatesh, ``When to Stop: On Optimal Stopping and Effective Machine Size in Learning,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1993.

  35. G. Pancha and S. S. Venkatesh, ``Feature and Memory Selective Error Correction in Neural Associative Memory,in Associative Neural Memories: Theory and Implementation (ed. M. H. Hassoun). New York: Oxford University Press, 1993.

  36. S. S. Venkatesh, ``Directed Drift: A New Linear Threshold Algorithm for Learning Binary Weights On-Line,Journal of Computer and Systems Sciences, vol. 46, no. 2, pp. 198-217, 1993.

  37. P. Baldi and S. S. Venkatesh, ``Random Interconnections in Higher-Order Neural Networks,'' IEEE Transactions on Information Theory, vol. 39, no. 1, pp. 274-282, 1993.

  38. I. Hu and S. S. Venkatesh, ``On the Minimum Expected Duration of a Coin Tossing Game,'' IEEE Transactions on Information Theory, vol. 39, no. 2, pp. 581-593, 1993.

  39. S. S. Venkatesh, ``On Approximations of Functions by Depth-Two Neural Networks,'' IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994; reprinted in Proceedings 1994 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1994.

  40. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Predictions of the Finite-Sample Risk of the k-Nearest Neighbor Classifier,'' Proceedings of the 12th International Conference on Pattern Recognition, vol. 2, pp. 1-7. Los Alamitos, California: IEEE Computer Society Press, 1994.

  41. C. Wang and S. S. Venkatesh, ``Machine Size Selection for Optimal Generalisation,'' Workshop on Applications of Descriptional Complexity to Inductive, Statistical, and Visual Inference, New Brunswick, New Jersey, July 1994.

  42. D. Psaltis, R. Snapp, and S. S. Venkatesh, ``On the Finite Sample Performance of the Nearest Neighbour Algorithm,IEEE Transactions on Information Theory, vol. IT-40, pp. 820-837, 1994.

  43. C. Wang and S. S. Venkatesh, ``Temporal Dynamics of Generalization in Neural Networks,' Conference on Neural Information Processing Systems, Denver, Colorado, November 1994; reprinted in Advances in Neural Information Processing Systems 7, (eds. D. S. Touretzky, G. Tesauro, and T. K. Leen). Cambridge, MA: MIT Press, 1995.

  44. J. Ratsaby and S. S. Venkatesh, ``Learning from a Mixture of Labelled and Unlabelled Examples with Parametric Side-Information,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  45. C. Wang and S. S. Venkatesh, ``Criteria for Specifying Machine Complexity in Learning,in Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  46. R. R. Snapp and S. S. Venkatesh, ``k-Nearest Neighbors in Search of a Metric,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  47. C. Wang, S. S. Venkatesh, and J. S. Judd, ``Optimal Stopping and Effective Machine Complexity in Learning,IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  48. S. C. Fang and S. S. Venkatesh, ``On Batch Learning in a Binary Weight Setting,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  49. J. Ratsaby and S. S. Venkatesh, ``The Complexity of Learning from a Mixture of Labelled and Unlabelled Examples,'' in Proceedings of the Thirty-third Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October 1995.

  50. S. S. Venkatesh, ``Connectivity and Capacity in the Hebb Rule,'' in Advances in Neural Networks, (eds. A. Orlitsky, V. Roychoudhury, and S. Siu). New York: Kluver, 1995.

  51. S. B. Bulumulla and S. S. Venkatesh, ``On the Quantized Decorrelating Detector,'' Conference on Information Sciences and Systems, Princeton University, Princeton, New Jersey, March 1996.

  52. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' in Proceedings of the International Conference on Neural Networks, Perth, Australia, November 1996.

  53. A. Orlitsky and S. S. Venkatesh, ``On edge-colored interior planar graphs on a circle and the expected number of RNA secondary structures,'' Discrete Applied Mathematics, vol. 64, pp. 151-178, 1996.

  54. S. C. Fang and S. S. Venkatesh, ``Learning Binary Perceptrons Perfectly Efficiently,'' Journal of Computer and Systems Sciences, vol. 52, no. 2, pp. 374-389, 1996.

  55. S. B. Bulumulla, S. A. Kassam, and S. S. Venkatesh, ``Optimum and Sub-Optimum Receivers for OFDM Signals in Rayleigh Fading Channels,'' Conference on Information Sciences and Systems, Johns Hopkins University, Baltimore, Maryland, March 1997.

  56. S. C. Fang and S. S. Venkatesh, ``A Threshold Function for Harmonic Update,'' SIAM Journal of Discrete Mathematics, vol. 10, no. 3, pp. 482-498, 1997.

  57. S. B. Bulumulla, S. A. Kassam and S. S. Venkatesh, ``An Adaptive, Diversity Receiver for OFDM in Fading Channels,'' International Conference on Communications, Atlanta, GA, 1998.

  58. S. B. Bulumulla, S. A. Kassam and S. S. Venkatesh, ``Pilot Symbol Assisted Diversity Reception for a Fading Channel,'' International Conference on Acoustics, Speech, and Signal Processing, Seattle, WA, 1998.

  59. S. C. Fang and S. S. Venkatesh, ``The Capacity of Majority Rule,'' Random Structures and Algorithms, vol. 12, pp. 83-109, 1998.

  60. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Expansions of the k-Nearest Neighbor Risk,'' Annals of Statistics, vol. 26, no. 3, pp. 850-878, 1998.

  61. S. R. Kulkarni, G. Lugosi, and S. S. Venkatesh, ``Learning Pattern Classification—A Survey,' IEEE Transactions on Information Theory, Special Commemorative Issue 1948-1998, vol. 44, no. 6, pp. 2178-2206, 1998.

  62. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' Random Structures and Algorithms, vol. 14, pp. 345-381, 1999.

  63. S. S. Venkatesh, ``CDMA Capacity,'' 2000 Conference on Information Sciences and Systems, Princeton University, March 15-17, 2000.

  64. S. S. Venkatesh and J. Ratsaby, ``On Partially Blind Learning Complexity,'' Special Session on Statistical Learning, ISCAS 2000, Geneva, May 28-31, 2000.

  65. S. B. Bulumulla, S. A. Kassam, and S. S. Venkatesh, ``Joint Channel Estimation and Detection for OFDM Signals in a Rayleigh Fading Channel,'' IEEE Transactions on Communications, vol. 48, n0. 5, pp. 725–728, May 2000.

  66. C. Gunter, S. Khanna, K. Tan, and S. S. Venkatesh, ``DoS Protection for Reliably Authenticated Broadcast,'' 11th Annual Networkand Distributed System Security Symposium, San Diego, California, February 2004.

  67. S. S. Kunniyur and S. S. Venkatesh, ``Network Devolution and the Growth of Sensory Lacunae in Sensor Networks,'' WiOpt04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, University of Cambridge, UK, March 2004.

  68. S. S. Kunniyur and S. S. Venkatesh, ``Sensor Network Devolution and Breakdown in Survivor Connectivity,'' 2004 International Symposium on Information Theory, Chicago, Illinois, June 27-July 2, 2004.

  69. S. S. Venkatesh, ``Connectivity of Metric Random Graphs,' unpublished notes, 2004.

  70. Jae H. Song, Santosh S. Venkatesh, Emily. A. Conant, Ted W. Cary, Peter H. Arger, and Chandra M. Sehgal, ``Artificial Neural Network to Aid Differentiation Between Malignant and Benign Breast Masses by Ultrasound Imaging,' Ultrasonic Imaging and Signal Processing, SPIE Conference on Medical Imaging, San Diego, California, February 12--17, 2005.

  71. Jae H. Song, Santosh S. Venkatesh, Emily A. Conant, Peter H. Arger, Chandra M. Sehgal, ``Comparative Analysis of Logistic Regression and Artificial Neural Network for Computer-Aided Diagnosis of Breast Masses,' Academic Radiology, vol.~12, pp.~487--495, 2005.

  72. M. Sherr, M. Greenwald, C. A. Gunter, S. Khanna, and S. S. Venkatesh, ``Mitigating DoS Attack Through Selective Bin Verification,’’ Proceedings of the Workshop on Secure Network Protocols (NPSec), Boston, Massachusetts, November 2005.

  73. S. S. Venkatesh, ``Connectivity, Devolution, and Lacunae in Geometric Random Digraphs,'' Proceedings of the Inaugural Workshop on Information Theory and Applications, San Diego, CA, February 2006.

  74. S. S. Kunniyur and S. S. Venkatesh, ``Threshold Functions, Node Isolation, and Emergent Lacunae in Sensor Networks,' IEEE Transactions on Information Theory, vol.~52, no.~12, pp.~53525372, December 2006.

  75. M. B. Greenwald, S. Khanna, and S. S. Venkatesh, ``How Much Bandwidth Can Attack Bots Commandeer?' Proceedings of the Workshop on Information Theory and Applications, San Diego, CA, January 2007.

  76. S. Khanna, S. S. Venkatesh, O. Fatemieh, F. Khan, and C. A. Gunter, ``Adaptive Selective Verification,'' Proceedings of IEEE INFOCOM 2008, Phoneix, AZ, April 2008.

  77. Z. Liu, S. S. Venkatesh, and C. C. Maley, ``Sequence Space Coverage, Entropy of Genomes and the Potential to Detect Non-Human DNA in Human Samples,'' BMC Genomics, vol. 9, pp. 509–526, October 2008.

  78. S. S. Venkatesh, S. Khanna, O. Fatemieh, F. Khan, and C. A. Gunter, ``Nimble Clients Thwart Versatile DDoS Adversaries,'' Information Theory and Applications Workshop, San Diego, CA, 2009.

  79. S. S. Venkatesh, ``A Variation on the Littlewood-Offord Theme with Applications to Phase Transitions in CDMA Detection,'' Information Theory and Applications Workshop, San Diego, CA, 2011.

  80. M. H. R. Khouzani, S. S. Venkatesh, and S. Sarkar, ``Market-Based Control of Epidemics,''  49th Annual Allerton Conference on Communication, Control, and Computing, Allerton Retreat Centre, Monticello, IL, September 2011.

  81. S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh , ``Optimal Patching in Clustered Epidemics of Malware,'' Information Theory and Applications Workshop, San Diego, CA, February 2012.

  82. M. H. R. Khouzani, S. Eshghi, S. Sarkar, S. S. Venkatesh, and N. B. Shroff, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' 13th Annual ACM Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2012), Hilton Head, SC, June 2012.

  83. S. Khanna, S. S. Venkatesh, O. Fatemieh, F. Khan, and C. A. Gunter, ``Adaptive Selective Verification: An Efficient Adaptive Countermeasure to Thwart DoS Attacks,''IEEE Transactions on Networking, vol. 20, issue 3, pp. 715–728, June 2012.

  84. T. W. Cary, A. Cwanger, S. S. Venkatesh, E. F. Conant, and C. M. Sehgal, ``Comparison of Naïve Bayes and Logistic Regression for Computer-Aided Diagnosis of Breast Masses Using Ultrasound Imaging,'' Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy (eds. Johan G. Bosch and Marvin M. Doyley), Proceedings of SPIE, vol. 8320, pp. 83200M-1 to 83200M-7, 2012.

  85. R. Potharaju, E. Hoque, C. Nita-Rotaru, S. Sarkar, and S. S. Venkatesh, ``Closing the Pandora's Box: Defenses for Thwarting Epidemic Outbreaks in Mobile Adhoc Networks,'' 9th IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS 2012), Las Vegas, NV, October 2012.

  86. C. M. Sehgal, T. W. Cary, A. Cwanger, B. Levenback, S. S. Venkatesh, ``Combined Naïve Bayes and Logistic Regression for Quantitative Breast Sonography,'' 2012 IEEE International Ultrasonics Symposium, Dresden, Germany, October 2012.

  87. L. Yan, R. Guerin, K. Hosanagar, Y. Tan, and S. S. Venkatesh, ``Online Social Interactions and Opinion Formation,'' 22nd Annual Workshop on Information Technologies and Systems, Orlando, FL, December 2012.

  88. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,''  Information Theory and Applications Workshop, San Diego, CA, February 2013.

  89. S. S. Venkatesh, ``Opinion Formation in Ising Networks and Some Connections to the Littlewood-Offord Problem,'' Fourth Nordic Workshop on System and Networks Optimization for Wireless (SNOW 2013), Äkäslompolo, Finland, April 2013.

  90. C. M. Sehgal, L. Sultan, B. Levenback, S. S. Venkatesh, ``Statistical Methods for Breast Mass Classification by Ultrasound Imaging,'' 55th Annual Meeting and Exhibition of the American Association of Physicists in Medicine, Indianapolis, Indiana, August 2013

  91. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Spin Glasses with Attitude: Opinion Formation in a Partisan Erdös–Rényi World,'' Information Theory and Applications Workshop, San Diego, CA, February 2014.

  92. G. Bouzghar, B. J. Levenback, L. R. Sultan, S. S. Venkatesh, A. Cwanger, E. F. Conant, and C. M. Sehgal, ``Bayesian Probability of Malignancy With Breast Imaging Reporting and Data System Sonographic Features,'' Journal of Ultrasound in Medicine, vol. 33, pp. 641–648, 2014.

  93. E. Hoque, R. Potharaju, C. Nita-Rotaru, S. Sarkar, and S. S. Venkatesh, ``Taming Epidemic Outbreaks in Mobile Ad Hoc Networks,'' Ad Hoc Networks, vol. 24, part A, pp. 57–72, Elsevier, January 2015.

  94. L. R. Sultan, G. Bouzghar, B. J. Levenback, N. A. Faizi, S. S. Venkatesh, E. F. Conant, and C. M. Sehgal, ``Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses,'' Advances in Breast Cancer Research, vol. 4, no. 1, 8 pages, January 2015 (http://dx.doi.org/10.4236/abcr.2015.41001).

  95. S. Eshghi, S. Sarkar, and S. S. Venkatesh, ``Visibility-Aware Optimal Contagion of Malware Epidemics,'' Information Theory and Applications Workshop, San Diego, CA, February 2015.

  96. S. Eshghi, M. H. R. Khouzani, S. Sarkar, N. B. Shroff, and S. S. Venkatesh, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' IEEE Transactions on Automatic Control, vol. 60, no. 6, pp. 1554–1569, June 2015.

  97. S. S. Venkatesh, B. J. Levenback, L. R. Sultan, G. Bhouzghar, and C. M. Sehgal, ``Going Beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis Using Adaptive Boosting and Selective Pruning,'' Journal of Ultrasound in Medicine and Biology, vol. 41, issue 12, pp. 3148–3162, December 2015.

  98. S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh, ``Optimal Patching in Clustered Epidemics of Malware,'' IEEE/ACM Transactions on Networking, vol. 24, no. 1, pp. 283–298, February 2016.

  99. S. Eshghi, S. Sarkar, V. M. Preciado, S. S. Venkatesh, Q. Zhao, R. D'Souza, and A. Swami, ``Spread, then Target, and Advertise in Waves: Optimal Capital Allocation Across Advertising Channels,'' Information Theory and Applications Workshop, San Diego, CA, February 2017.

  100. L. R. Sultan, S. S. Venkatesh, E. Conant, and C. M. Sehgal, ``Quantitative Doppler Vascularity Improves Computer-Based Sonographic Diagnosis of Breast Cancer,'' Annual Convention of the American Institute of Ultrasound in Medicine (AIUM), Orlando, Florida, March 2017.

  101. S. S. Venkatesh, ``Remarks at Commencement by the Chair of the Faculty Senate: The Continuing Quest for Knowledge,'' The Almanac of the University of Pennsylvania, vol. 63, issue 35, Supplement p. V, May 23, 2017.

  102. S. S. Venkatesh, ``Welcome Back From the Senate Chair: Knowledge as a Beacon,’' The Almanac of the University of Pennsylvania, vol. 64, issue 2, August 29, 2017.

  103. S. Eshghi, S. Sarkar, and S. S. Venkatesh, ``Visibility-Aware Optimal Contagion of Malware Epidemics,'' IEEE Transactions on Automatic Control, vol. 62, issue 10, pp. 5205–5212, October 2017. Print ISSN: 0018–9286. Online ISSN 1558–2523. Digital Object Identifier: 10.1109/TAC.2016.2632426.

  104. L. W. Perna, S. S. Venkatesh, and J. A. Pinto-Martin, ``Five Tax Changes that will Hurt U.S. Higher Education: A Letter to Members of Congress from the Leaders of Penn's Faculty Senate,'' Medium, December 6, 2017: https://medium.com/@lauraperna1/five-tax-changes-that-will-hurt-u-s-higher-education-239072371409.

  105. J. A. Pinto-Martin, S. S. Venkatesh, and L. W. Perna, ``2018 Penn Teach-In: The Production, Dissemination, and Use of Knowledge, March 18–22,'' The Almanac of the University of Pennsylvania, volume 64, issue 26, March 13, 2018.

  106. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,'' Online Social Networks and Media, vol. 5, pp. 1–22, March 2018.

  107. S. S. Venkatesh, ``Report of the Chair of the Faculty Senate,’' The Almanac of the University of Pennsylvania: Supplements, vol. 64, issue 34, May 8, 2018.

  108. S. Eshghi, V. Preciado, S. Sarkar, S. S. Venkatesh, Q. Zhao, R. D'Souza, and A. Swami, ``Spread, then Target, and Advertise in Waves: Optimal Budget Allocation Across Advertising Channels,'' Transactions on Network Science and Engineering, Print ISSN: 2327–4697, Online ISSN: 2327–4697, vol. 7, issue 2, October 2, 2018: Digital Object Identifier: 10.1109/TNSE.2018.2873281.

  109. J. Kim, S. Sarkar, S. S. Venkatesh, M. Ryerson, and D. Starobinski, ``Modelling Information Propagation in General V2V-Enabled Transportation Networks,'' Information Theory and Applications Workshop, San Diego, CA, February 2019.

  110. J. Kim, S. Sarkar, S. S. Venkatesh, M. S. Ryerson, and D. Starobinski ``An Epidemiological Diffusion Framework for Vehicular Messaging in General Transportation Networks,'' Transportation Research Part B: Methodological, Special Issue on Innovative Shared Transportation, vol. 131, pp. 160–190, January 2020.

  111. A. F. Moustafa, T. W. Cary, L. R. Sultan, S. S. Venkatesh, C. M. Sehgal, ``Color Doppler Ultrasound Improves Performance of Machine Learning Diagnosis of Breast Cancer,'' Diagnostics, vol. 10, pp. 631–646, 2020: doi:10.3390/diagnostics10090631.

  112. J. Kim, R. Saraogi, S. Sarkar, S. S. Venkatesh, ``Modeling the Impact of Traffic Signals on V2V Information Flow,'' 2020 IEEE 91st Vehicular Technology Conference [online], (VTC2020-Spring).

  113. V. Amelkin, R. Vohra, and S. S. Venkatesh, ``Structure and Dynamics of Contagion in Financial Networks with Implications for Systemic Risk,''  Sixth Annual Conference on Network Science in Economics, Chicago, IL, March 2021.

  114. C. M. Sehgal, S. S. Venkatesh, and L. M. Sultan, ``Machine Implemented Methods, Systems, and Apparatuses for Improving Diagnostic Performance’’, United States Patent:  US 11,071,517 B2, published July 27, 2021.

  115. V. Amelkin, R. Vohra, and S. S. Venkatesh, ``Contagion and Equilibria in Diversified Financial Networks,'' Economic Theory Conference VI, Becker—Friedman Institute, University of Chicago, Chicago, IL, August 2021.

  116. V. Amelkin, S. S. Venkatesh, and R. Vohra, ``Contagion and Equilibria in Diversified Financial Networks,'' Econometrica, submitted November 2021.

  117. A. DasGupta, T. M. Sellke, and S. S. Venkatesh, ``The Exact and Asymptotic Distributions of X mod k and Connections to Tauberian Theorems, Congruence Algebra, and Fourier Analysis,'’ unpublished, 2021.

  118. L. R. Sultan, T. W. Cary, M. Al-Hasani, M. B. Karmacharya, S. S. Venkatesh, C-A. Assenmacher, E. Radaelli, and C. M. Sehgal, ``Can sequential images from the same object be used for training machine learning models? A case study for detecting liver disease by ultrasound radiomics,'' Artificial Intelligence, submitted July 2022.

  119. K. Dasaratha, S. S. Venkatesh, and R. Vohra, ``Financial Contagion in Stochastic Block Graphons'', Seventh Annual Conference on Network Sciences and Economics, Booth School of Business, University of Chicago, Chicago, IL, March 18–20, 2022.

  120. S. S. Venkatesh, ``Clarity Amid Catastrophe — Understanding Chance'', TEDx Penn 2022: ÆFFECT, University of Pennsylvania, Philadelphia, PA, April 16, 2022.

  121. S. S. Venkatesh, ``From the Sad Story of a Birthday Cake to Social Distancing, Political Turmoil, and Pandemic,'' ASIME Keynote, Adelphi University, New York, July 7, 2022.

  122. L. R. Sultan, T. W. Cary, M. Al-Hasani, M. B. Karmacharya, S. S. Venkatesh, C-A. Assenmacher, E. Radaelli, and C. M. Sehgal, ``Can sequential images from the same object be used for training machine learning models? A case study for detecting liver disease by ultrasound radiomics​,'' Artificial Intelligence, vol.~3, pp.~739--750, September 2022: https://doi.org/10.3390/ai3030043.

 

By topic
Probability, information theory, combinatorics

 
  1. S. S. Venkatesh, ``Epsilon Capacity of Neural Networks,'' Conference on Neural Networks for Computing, Snowbird, Utah, April 1986; reprinted in Neural Networks for Computing, (ed. J. Denker). New York: AIP, 1986.

  2. S. S. Venkatesh, ``Computation with Neural Networks,'' Proceedings of the Ninth Annual Conference on Engineering in Medicine and Biology, Boston, Massachusetts, 1987, pp. 1364-1365.

  3. R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, ``The Capacity of the Hopfield Associative Memory,'' IEEE Transactions on Information Theory, vol. IT-33, pp. 461-482, 1987.

  4. P. Baldi and S. S. Venkatesh, ``Number of Stable Points for Spin Glasses and Neural Networks of Higher Orders,'' Physical Review Letters, vol. 58, pp. 913-916, 1987.

  5. P. Baldi and S. S. Venkatesh, ``On Properties of Networks of Neuron-Like Elements,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1987; reprinted in Neural Information Processing Systems, (ed. D. Anderson). New York: AIP, 1988.

  6. S. S. Venkatesh, D. Psaltis, and G. Sirat, ``A Class of Spectral Algorithms for Neural Associative Memory,IEEE International Symposium on Information Theory, Kobe, Japan, June 1988.

  7. D. Psaltis and S. S. Venkatesh, ``Information Storage in Fully Interconnected Networks,'' in Evolution, Learning, and Cognition, (ed. Y. C. Lee). Teaneck, New Jersey: World Scientific, 1989.

  8. S. S. Venkatesh and D. Psaltis, ``Linear and Logarithmic Capacities in Associative Neural Networks,'' IEEE Transactions on Information Theory, vol. IT-35, pp. 558-568, 1989.  CorrectionIEEE Transactions on Information Theory, vol. IT-53, p. 854, 2007.

  9. S. S. Venkatesh, ``What is the Capacity of One Bit of Memory?,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  10. S. S. Venkatesh, ``Computation and Learning in Neural Networks with Binary Weights,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  11. S. S. Venkatesh and D. Psaltis, ``Error Tolerance and Neural Capacity,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  12. S. S. Venkatesh, G. Pancha, D. Psaltis, and G. Sirat, ``Shaping Attraction Basins in Neural Networks,' Neural Networks, vol. 3, no. 6, pp. 613-624, 1990.

  13. S. S. Venkatesh and P. Baldi, ``Programmed Interactions in Higher-Order Neural Networks: Maximal Capacity,Journal of Complexity, vol. 7, no. 3, pp. 316-337, 1991.

  14. S. S. Venkatesh and J. Franklin, ``How Much Information Can One Bit of Memory Retain About a Bernoulli Sequence?'' IEEE Transactions on Information Theory, vol. IT-37, pp. 1595-1604, 1991.

  15. S. S. Venkatesh and P. Baldi, ``Programmed Interactions in Higher-Order Neural Networks: The Outer-Product Algorithm,'' Journal of Complexity, vol. 7, no. 4, pp. 443-479, 1991.

  16. R. Snapp, D. Psaltis, and S. S. Venkatesh, ``Asymptotic Slowing Down of the Nearest Neighbour Classifier,Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman), San Mateo, California: Morgan Kaufmann, 1991.

  17. S. Biswas and S. S. Venkatesh, ``The Devil and the Network: What Sparsity Implies to Robustness and Memory,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman). San Mateo, California: Morgan Kaufmann, 1991.

  18. S. S. Venkatesh, ``On Learning Binary Weights for Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, August 1991; reprinted in Proceedings of the Fourth Workshop on Computational Learning Theory, (eds. L. G. Valiant and M. K. Warmuth). San Mateo, California: Morgan Kaufmann, 1991.

  19. S. S. Venkatesh, R. Snapp, and D. Psaltis, ``Bellman Strikes Again—The Rate of Growth of Sample Complexity with Dimension for the Nearest Neighbour Classifier,' Workshop on Computational Learning Theory, University of Pittsburgh, Pittsburgh, Pennsylvania, July 1992; reprinted in Proceedings of the Fifth Workshop on Computational Learning Theory, Baltimore, Maryland: ACM Press, 1992.

  20. J. Ratsaby and S. S. Venkatesh, ``Learning with Few Labelled Examples,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1992.

  21. S. S. Venkatesh, ``Computation and Learning in the Context of Neural Network Capacity,'' in Neural Networks for Perception, (ed. H. Wechsler). New York: Academic Press, 1992.

  22. S. S. Venkatesh and D. Psaltis, ``On Reliable Computation with Formal Neurons,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 87-91, 1992.

  23. S. S. Venkatesh, ``The Science of Making Erors,'' IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 135-144, 1992.

  24. S. S. Venkatesh, ``Robustness in Neural Computation: Random Graphs and Sparsity,'' IEEE Transactions on Information Theory, vol. 38, no. 3, pp. 1114-1118, 1992.

  25. I. Hu and S. S. Venkatesh, ``On the Minimum Expected Duration of a Coin Tossing Game,'' IEEE International Symposium on Information Theory, San Antonio, Texas, January 1993; reprinted in Proceedings 1993 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1993.

  26. D. Psaltis, R. Snapp, and S. S. Venkatesh, ``On the Finite Sample Performance of the Nearest Neighbour Classifier,IEEE International Symposium on Information Theory, San Antonio, Texas, January 1993; reprinted in Proceedings 1993 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1993.

  27. S. C. Fang and S. S. Venkatesh, ``On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalisation in Learning Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1993; reprinted in Proceedings of the Sixth Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1993.

  28. B. Sarath and S. S. Venkatesh, ``Auditor Liability for Management Fraud,'' Fifth Annual Conference on Intelligent Systems in Accounting, Finance, and Management, Palo Alto, California, November 1993.

  29. C. Wang, S. J. Judd, and S. S. Venkatesh, ``When to Stop: On Optimal Stopping and Effective Machine Size in Learning,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1993.

  30. G. Pancha and S. S. Venkatesh, ``Feature and Memory Selective Error Correction in Neural Associative Memory,in Associative Neural Memories: Theory and Implementation (ed. M. H. Hassoun). New York: Oxford University Press, 1993.

  31. S. S. Venkatesh, ``Directed Drift: A New Linear Threshold Algorithm for Learning Binary Weights On-Line,Journal of Computer and Systems Sciences, vol. 46, no. 2, pp. 198-217, 1993.

  32. P. Baldi and S. S. Venkatesh, ``Random Interconnections in Higher-Order Neural Networks,'' IEEE Transactions on Information Theory, vol. 39, no. 1, pp. 274-282, 1993.

  33. I. Hu and S. S. Venkatesh, ``On the Minimum Expected Duration of a Coin Tossing Game,'' IEEE Transactions on Information Theory, vol. 39, no. 2, pp. 581-593, 1993.

  34. S. S. Venkatesh, ``On Approximations of Functions by Depth-Two Neural Networks,'' IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994; reprinted in Proceedings 1994 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1994.

  35. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Predictions of the Finite-Sample Risk of the k-Nearest Neighbor Classifier,'' Proceedings of the 12th International Conference on Pattern Recognition, vol. 2, pp. 1-7. Los Alamitos, California: IEEE Computer Society Press, 1994.

  36. C. Wang and S. S. Venkatesh, ``Machine Size Selection for Optimal Generalisation,'' Workshop on Applications of Descriptional Complexity to Inductive, Statistical, and Visual Inference, New Brunswick, New Jersey, July 1994.

  37. D. Psaltis, R. Snapp, and S. S. Venkatesh, ``On the Finite Sample Performance of the Nearest Neighbour Algorithm,IEEE Transactions on Information Theory, vol. IT-40, pp. 820-837, 1994.

  38. C. Wang and S. S. Venkatesh, ``Temporal Dynamics of Generalization in Neural Networks,' Conference on Neural Information Processing Systems, Denver, Colorado, November 1994; reprinted in Advances in Neural Information Processing Systems 7, (eds. D. S. Touretzky, G. Tesauro, and T. K. Leen). Cambridge, MA: MIT Press, 1995.

  39. J. Ratsaby and S. S. Venkatesh, ``Learning from a Mixture of Labelled and Unlabelled Examples with Parametric Side-Information,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  40. C. Wang and S. S. Venkatesh, ``Criteria for Specifying Machine Complexity in Learning,in Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  41. R. R. Snapp and S. S. Venkatesh, ``k-Nearest Neighbors in Search of a Metric,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  42. C. Wang, S. S. Venkatesh, and J. S. Judd, ``Optimal Stopping and Effective Machine Complexity in Learning,IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  43. S. C. Fang and S. S. Venkatesh, ``On Batch Learning in a Binary Weight Setting,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  44. J. Ratsaby and S. S. Venkatesh, ``The Complexity of Learning from a Mixture of Labelled and Unlabelled Examples,'' in Proceedings of the Thirty-third Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October 1995.

  45. S. S. Venkatesh, ``Connectivity and Capacity in the Hebb Rule,'' in Advances in Neural Networks, (eds. A. Orlitsky, V. Roychoudhury, and S. Siu). New York: Kluver, 1995.

  46. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' in Proceedings of the International Conference on Neural Networks, Perth, Australia, November 1996.

  47. A. Orlitsky and S. S. Venkatesh, ``On edge-colored interior planar graphs on a circle and the expected number of RNA secondary structures,'' Discrete Applied Mathematics, vol. 64, pp. 151-178, 1996.

  48. S. C. Fang and S. S. Venkatesh, ``Learning Binary Perceptrons Perfectly Efficiently,'' Journal of Computer and Systems Sciences, vol. 52, no. 2, pp. 374-389, 1996.

  49. S. C. Fang and S. S. Venkatesh, ``A Threshold Function for Harmonic Update,'' SIAM Journal of Discrete Mathematics, vol. 10, no. 3, pp. 482-498, 1997.

  50. S. C. Fang and S. S. Venkatesh, ``The Capacity of Majority Rule,'' Random Structures and Algorithms, vol. 12, pp. 83-109, 1998.

  51. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Expansions of the k-Nearest Neighbor Risk,'' Annals of Statistics, vol. 26, no. 3, pp. 850-878, 1998.

  52. S. R. Kulkarni, G. Lugosi, and S. S. Venkatesh, ``Learning Pattern Classification—A Survey,' IEEE Transactions on Information Theory, Special Commemorative Issue 1948-1998, vol. 44, no. 6, pp. 2178-2206, 1998.

  53. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' Random Structures and Algorithms, vol. 14, pp. 345-381, 1999.

  54. S. S. Venkatesh, ``CDMA Capacity,'' 2000 Conference on Information Sciences and Systems, Princeton University, March 15-17, 2000.

  55. S. S. Venkatesh and J. Ratsaby, ``On Partially Blind Learning Complexity,'' Special Session on Statistical Learning, ISCAS 2000, Geneva, May 28-31, 2000.

  56. S. S. Kunniyur and S. S. Venkatesh, ``Network Devolution and the Growth of Sensory Lacunae in Sensor Networks,'' WiOpt04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, University of Cambridge, UK, March 2004.

  57. S. S. Kunniyur and S. S. Venkatesh, ``Sensor Network Devolution and Breakdown in Survivor Connectivity,'' 2004 International Symposium on Information Theory, Chicago, Illinois, June 27-July 2, 2004.

  58. S. S. Venkatesh, ``Connectivity of Metric Random Graphs,' unpublished notes, 2004.

  59. S. S. Venkatesh, ``Connectivity, Devolution, and Lacunae in Geometric Random Digraphs,'' Proceedings of the Inaugural Workshop on Information Theory and Applications, San Diego, CA, February 2006.

  60. S. S. Kunniyur and S. S. Venkatesh, ``Threshold Functions, Node Isolation, and Emergent Lacunae in Sensor Networks,' IEEE Transactions on Information Theory, vol.~52, no.~12, pp.~53525372, December 2006.

  61. Z. Liu, S. S. Venkatesh, and C. C. Maley, ``Sequence Space Coverage, Entropy of Genomes and the Potential to Detect Non-Human DNA in Human Samples,'' BMC Genomics, vol. 9, pp. 509–526, October 2008.

  62. S. S. Venkatesh, ``A Variation on the Littlewood-Offord Theme with Applications to Phase Transitions in CDMA Detection,'' Information Theory and Applications Workshop, San Diego, CA, 2011.

  63. L. Yan, R. Guerin, K. Hosanagar, Y. Tan, and S. S. Venkatesh, ``Online Social Interactions and Opinion Formation,'' 22nd Annual Workshop on Information Technologies and Systems, Orlando, FL, December 2012.

  64. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,''  Information Theory and Applications Workshop, San Diego, CA, February 2013.

  65. S. S. Venkatesh, ``Opinion Formation in Ising Networks and Some Connections to the Littlewood-Offord Problem,'' Fourth Nordic Workshop on System and Networks Optimization for Wireless (SNOW 2013), Äkäslompolo, Finland, April 2013.

  66. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Spin Glasses with Attitude: Opinion Formation in a Partisan Erdös–Rényi World,'' Information Theory and Applications Workshop, San Diego, CA, February 2014.

  67. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,'' Online Social Networks and Media, vol. 5, pp. 1–22, March 2018.

  68. V. Amelkin, R. Vohra, and S. S. Venkatesh, ``Structure and Dynamics of Contagion in Financial Networks with Implications for Systemic Risk,''  Sixth Annual Conference on Network Science in Economics, Chicago, IL, March 2021.

  69. V. Amelkin, R. Vohra, and S. S. Venkatesh, ``Contagion and Equilibria in Diversified Financial Networks,'' Economic Theory Conference VI, Becker--Friedman Institute, University of Chicago, Chicago, IL, August 2021.

  70. V. Amelkin, S. S. Venkatesh, and R. Vohra, ``Contagion and Equilibria in Diversified Financial Networks,'' Econometrica, submitted November 2021.

  71. A. DasGupta, T. M. Sellke, and S. S. Venkatesh, ``The Exact and Asymptotic Distributions of X mod k and Connections to Tauberian Theorems, Congruence Algebra, and Fourier Analysis,'’ unpublished, 2021.

  72. K. Dasaratha, S. S. Venkatesh, and R. Vohra, ``Financial Contagion in Stochastic Block Graphons'', Seventh Annual Conference on Network Sciences and Economics, Booth School of Business, University of Chicago, Chicago, IL, March 18–20, 2022.

  73. S. S. Venkatesh, ``Clarity Amid Catastrophe — Understanding Chance'', TEDx Penn 2022: ÆFFECT, University of Pennsylvania, Philadelphia, PA, April 16, 2022.

  74. S. S. Venkatesh, ``From the Sad Story of a Birthday Cake to Social Distancing, Political Turmoil, and Pandemic,'' ASIME Keynote, Adelphi University, New York, July 7, 2022.

 

By topic
Neural Networks

  1. S. S. Venkatesh, ``Epsilon Capacity of Neural Networks,'' Conference on Neural Networks for Computing, Snowbird, Utah, April 1986; reprinted in Neural Networks for Computing, (ed. J. Denker). New York: AIP, 1986.

  2. D. Psaltis, J. Hong, and S. S. Venkatesh, ``Shift-Invariance in Optical Associative Memories,'' in Proceedings of SPIE: First Annual Symposium on Optoelectronics and Laser Applications, Los Angeles, California, 1986.

  3. S. S. Venkatesh, ``Computation with Neural Networks,'' Proceedings of the Ninth Annual Conference on Engineering in Medicine and Biology, Boston, Massachusetts, 1987, pp. 1364-1365.

  4. R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, ``The Capacity of the Hopfield Associative Memory,'' IEEE Transactions on Information Theory, vol. IT-33, pp. 461-482, 1987.

  5. P. Baldi and S. S. Venkatesh, ``Number of Stable Points for Spin Glasses and Neural Networks of Higher Orders,'' Physical Review Letters, vol. 58, pp. 913-916, 1987.

  6. P. Baldi and S. S. Venkatesh, ``On Properties of Networks of Neuron-Like Elements,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1987; reprinted in Neural Information Processing Systems, (ed. D. Anderson). New York: AIP, 1988.

  7. S. S. Venkatesh, D. Psaltis, and G. Sirat, ``A Class of Spectral Algorithms for Neural Associative Memory,IEEE International Symposium on Information Theory, Kobe, Japan, June 1988.

  8. D. Psaltis and S. S. Venkatesh, ``Information Storage in Fully Interconnected Networks,'' in Evolution, Learning, and Cognition, (ed. Y. C. Lee). Teaneck, New Jersey: World Scientific, 1989.

  9. S. S. Venkatesh and D. Psaltis, ``Linear and Logarithmic Capacities in Associative Neural Networks,'' IEEE Transactions on Information Theory, vol. IT-35, pp. 558-568, 1989.  CorrectionIEEE Transactions on Information Theory, vol. IT-53, p. 854, 2007.

  10. S. S. Venkatesh, ``What is the Capacity of One Bit of Memory?,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  11. S. S. Venkatesh, ``Computation and Learning in Neural Networks with Binary Weights,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  12. S. S. Venkatesh and D. Psaltis, ``Error Tolerance and Neural Capacity,'' IEEE International Symposium on Information Theory, San Diego, California, January 1990.

  13. S. S. Venkatesh, G. Pancha, D. Psaltis, and G. Sirat, ``Shaping Attraction Basins in Neural Networks,' Neural Networks, vol. 3, no. 6, pp. 613-624, 1990.

  14. S. S. Venkatesh and P. Baldi, ``Programmed Interactions in Higher-Order Neural Networks: Maximal Capacity,Journal of Complexity, vol. 7, no. 3, pp. 316-337, 1991.

  15. S. S. Venkatesh and P. Baldi, ``Programmed Interactions in Higher-Order Neural Networks: The Outer-Product Algorithm,'' Journal of Complexity, vol. 7, no. 4, pp. 443-479, 1991.

  16. S. Biswas and S. S. Venkatesh, ``The Devil and the Network: What Sparsity Implies to Robustness and Memory,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman). San Mateo, California: Morgan Kaufmann, 1991.

  17. S. S. Venkatesh, ``On Learning Binary Weights for Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, August 1991; reprinted in Proceedings of the Fourth Workshop on Computational Learning Theory, (eds. L. G. Valiant and M. K. Warmuth). San Mateo, California: Morgan Kaufmann, 1991.

  18. S. S. Venkatesh, ``Computation and Learning in the Context of Neural Network Capacity,'' in Neural Networks for Perception, (ed. H. Wechsler). New York: Academic Press, 1992.

  19. S. S. Venkatesh and D. Psaltis, ``On Reliable Computation with Formal Neurons,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 87-91, 1992.

  20. S. S. Venkatesh, ``The Science of Making Erors,'' IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 135-144, 1992.

  21. S. S. Venkatesh, ``Robustness in Neural Computation: Random Graphs and Sparsity,'' IEEE Transactions on Information Theory, vol. 38, no. 3, pp. 1114-1118, 1992.

  22. S. C. Fang and S. S. Venkatesh, ``On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalisation in Learning Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1993; reprinted in Proceedings of the Sixth Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1993.

  23. C. Wang, S. J. Judd, and S. S. Venkatesh, ``When to Stop: On Optimal Stopping and Effective Machine Size in Learning,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1993.

  24. G. Pancha and S. S. Venkatesh, ``Feature and Memory Selective Error Correction in Neural Associative Memory,in Associative Neural Memories: Theory and Implementation (ed. M. H. Hassoun). New York: Oxford University Press, 1993.

  25. S. S. Venkatesh, ``Directed Drift: A New Linear Threshold Algorithm for Learning Binary Weights On-Line,Journal of Computer and Systems Sciences, vol. 46, no. 2, pp. 198-217, 1993.

  26. P. Baldi and S. S. Venkatesh, ``Random Interconnections in Higher-Order Neural Networks,'' IEEE Transactions on Information Theory, vol. 39, no. 1, pp. 274-282, 1993.

  27. S. S. Venkatesh, ``On Approximations of Functions by Depth-Two Neural Networks,'' IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994; reprinted in Proceedings 1994 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1994.

  28. C. Wang and S. S. Venkatesh, ``Machine Size Selection for Optimal Generalisation,'' Workshop on Applications of Descriptional Complexity to Inductive, Statistical, and Visual Inference, New Brunswick, New Jersey, July 1994.

  29. C. Wang and S. S. Venkatesh, ``Temporal Dynamics of Generalization in Neural Networks,' Conference on Neural Information Processing Systems, Denver, Colorado, November 1994; reprinted in Advances in Neural Information Processing Systems 7, (eds. D. S. Touretzky, G. Tesauro, and T. K. Leen). Cambridge, MA: MIT Press, 1995.

  30. C. Wang and S. S. Venkatesh, ``Criteria for Specifying Machine Complexity in Learning,in Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  31. C. Wang, S. S. Venkatesh, and J. S. Judd, ``Optimal Stopping and Effective Machine Complexity in Learning,IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  32. S. C. Fang and S. S. Venkatesh, ``On Batch Learning in a Binary Weight Setting,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  33. S. S. Venkatesh, ``Connectivity and Capacity in the Hebb Rule,'' in Advances in Neural Networks, (eds. A. Orlitsky, V. Roychoudhury, and S. Siu). New York: Kluver, 1995.

  34. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' in Proceedings of the International Conference on Neural Networks, Perth, Australia, November 1996.

  35. S. C. Fang and S. S. Venkatesh, ``Learning Binary Perceptrons Perfectly Efficiently,'' Journal of Computer and Systems Sciences, vol. 52, no. 2, pp. 374-389, 1996.

  36. S. C. Fang and S. S. Venkatesh, ``A Threshold Function for Harmonic Update,'' SIAM Journal of Discrete Mathematics, vol. 10, no. 3, pp. 482-498, 1997.

  37. S. C. Fang and S. S. Venkatesh, ``The Capacity of Majority Rule,'' Random Structures and Algorithms, vol. 12, pp. 83-109, 1998.

  38. S. R. Kulkarni, G. Lugosi, and S. S. Venkatesh, ``Learning Pattern Classification—A Survey,' IEEE Transactions on Information Theory, Special Commemorative Issue 1948-1998, vol. 44, no. 6, pp. 2178-2206, 1998.

  39. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' Random Structures and Algorithms, vol. 14, pp. 345-381, 1999.

  40. Jae H. Song, Santosh S. Venkatesh, Emily. A. Conant, Ted W. Cary, Peter H. Arger, and Chandra M. Sehgal, ``Artificial Neural Network to Aid Differentiation Between Malignant and Benign Breast Masses by Ultrasound Imaging,' Ultrasonic Imaging and Signal Processing, SPIE Conference on Medical Imaging, San Diego, California, February 12--17, 2005.

  41. Jae H. Song, Santosh S. Venkatesh, Emily A. Conant, Peter H. Arger, Chandra M. Sehgal, ``Comparative Analysis of Logistic Regression and Artificial Neural Network for Computer-Aided Diagnosis of Breast Masses,' Academic Radiology, vol.~12, pp.~487--495, 2005.

  42. L. Yan, R. Guerin, K. Hosanagar, Y. Tan, and S. S. Venkatesh, ``Online Social Interactions and Opinion Formation,'' 22nd Annual Workshop on Information Technologies and Systems, Orlando, FL, December 2012.

  43. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,''  Information Theory and Applications Workshop, San Diego, CA, February 2013.

  44. S. S. Venkatesh, ``Opinion Formation in Ising Networks and Some Connections to the Littlewood-Offord Problem,'' Fourth Nordic Workshop on System and Networks Optimization for Wireless (SNOW 2013), Äkäslompolo, Finland, April 2013.

  45. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Spin Glasses with Attitude: Opinion Formation in a Partisan Erdös–Rényi World,'' Information Theory and Applications Workshop, San Diego, CA, February 2014.

  46. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,'' Online Social Networks and Media, vol. 5, pp. 1–22, March 2018.

 

By topic
Pattern recognition, machine learning

  1. R. Snapp, D. Psaltis, and S. S. Venkatesh, ``Asymptotic Slowing Down of the Nearest Neighbour Classifier,Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman), San Mateo, California: Morgan Kaufmann, 1991.

  2. S. Biswas and S. S. Venkatesh, ``The Devil and the Network: What Sparsity Implies to Robustness and Memory,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1990; reprinted in Advances in Neural Information Processing Systems 3, (eds. D. S. Touretzky and R. Lippman). San Mateo, California: Morgan Kaufmann, 1991.

  3. S. S. Venkatesh, ``On Learning Binary Weights for Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, August 1991; reprinted in Proceedings of the Fourth Workshop on Computational Learning Theory, (eds. L. G. Valiant and M. K. Warmuth). San Mateo, California: Morgan Kaufmann, 1991.

  4. S. S. Venkatesh, R. Snapp, and D. Psaltis, ``Bellman Strikes Again--The Rate of Growth of Sample Complexity with Dimension for the Nearest Neighbour Classifier,' Workshop on Computational Learning Theory, University of Pittsburgh, Pittsburgh, Pennsylvania, July 1992; reprinted in Proceedings of the Fifth Workshop on Computational Learning Theory, Baltimore, Maryland: ACM Press, 1992.

  5. J. Ratsaby and S. S. Venkatesh, ``Learning with Few Labelled Examples,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1992.

  6. S. S. Venkatesh, ``Computation and Learning in the Context of Neural Network Capacity,'' in Neural Networks for Perception, (ed. H. Wechsler). New York: Academic Press, 1992.

  7. D. Psaltis, R. Snapp, and S. S. Venkatesh, ``On the Finite Sample Performance of the Nearest Neighbour Classifier,IEEE International Symposium on Information Theory, San Antonio, Texas, January 1993; reprinted in Proceedings 1993 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1993.

  8. S. C. Fang and S. S. Venkatesh, ``On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalisation in Learning Majority Functions,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1993; reprinted in Proceedings of the Sixth Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1993.

  9. C. Wang, S. J. Judd, and S. S. Venkatesh, ``When to Stop: On Optimal Stopping and Effective Machine Size in Learning,'' Conference on Neural Information Processing Systems, Denver, Colorado, November 1993.

  10. G. Pancha and S. S. Venkatesh, ``Feature and Memory Selective Error Correction in Neural Associative Memory,in Associative Neural Memories: Theory and Implementation (ed. M. H. Hassoun). New York: Oxford University Press, 1993.

  11. S. S. Venkatesh, ``Directed Drift: A New Linear Threshold Algorithm for Learning Binary Weights On-Line,Journal of Computer and Systems Sciences, vol. 46, no. 2, pp. 198-217, 1993.

  12. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Predictions of the Finite-Sample Risk of the k-Nearest Neighbor Classifier,'' Proceedings of the 12th International Conference on Pattern Recognition, vol. 2, pp. 1-7. Los Alamitos, California: IEEE Computer Society Press, 1994.

  13. C. Wang and S. S. Venkatesh, ``Machine Size Selection for Optimal Generalisation,'' Workshop on Applications of Descriptional Complexity to Inductive, Statistical, and Visual Inference, New Brunswick, New Jersey, July 1994.

  14. D. Psaltis, R. Snapp, and S. S. Venkatesh, ``On the Finite Sample Performance of the Nearest Neighbour Algorithm,IEEE Transactions on Information Theory, vol. IT-40, pp. 820-837, 1994.

  15. C. Wang and S. S. Venkatesh, ``Temporal Dynamics of Generalization in Neural Networks,' Conference on Neural Information Processing Systems, Denver, Colorado, November 1994; reprinted in Advances in Neural Information Processing Systems 7, (eds. D. S. Touretzky, G. Tesauro, and T. K. Leen). Cambridge, MA: MIT Press, 1995.

  16. J. Ratsaby and S. S. Venkatesh, ``Learning from a Mixture of Labelled and Unlabelled Examples with Parametric Side-Information,'' Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  17. C. Wang and S. S. Venkatesh, ``Criteria for Specifying Machine Complexity in Learning,in Workshop on Computational Learning Theory, University of California, Santa Cruz, California, July 1995; reprinted in Proceedings of the Eighth Annual Workshop on Computational Learning Theory. Baltimore, Maryland: ACM Press, 1995.

  18. R. R. Snapp and S. S. Venkatesh, ``k-Nearest Neighbors in Search of a Metric,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  19. C. Wang, S. S. Venkatesh, and J. S. Judd, ``Optimal Stopping and Effective Machine Complexity in Learning,IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  20. S. C. Fang and S. S. Venkatesh, ``On Batch Learning in a Binary Weight Setting,'' IEEE International Symposium on Information Theory, Whistler, Canada, September 1995; reprinted in Proceedings 1995 IEEE International Symposium on Information Theory. Piscataway, New Jersey: IEEE Press, 1995.

  21. J. Ratsaby and S. S. Venkatesh, ``The Complexity of Learning from a Mixture of Labelled and Unlabelled Examples,'' in Proceedings of the Thirty-third Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October 1995.

  22. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' in Proceedings of the International Conference on Neural Networks, Perth, Australia, November 1996.

  23. S. C. Fang and S. S. Venkatesh, ``Learning Binary Perceptrons Perfectly Efficiently,'' Journal of Computer and Systems Sciences, vol. 52, no. 2, pp. 374-389, 1996.

  24. S. C. Fang and S. S. Venkatesh, ``A Threshold Function for Harmonic Update,'' SIAM Journal of Discrete Mathematics, vol. 10, no. 3, pp. 482-498, 1997.

  25. S. C. Fang and S. S. Venkatesh, ``The Capacity of Majority Rule,'' Random Structures and Algorithms, vol. 12, pp. 83-109, 1998.

  26. R. R. Snapp and S. S. Venkatesh, ``Asymptotic Expansions of the k-Nearest Neighbor Risk,'' Annals of Statistics, vol. 26, no. 3, pp. 850-878, 1998.

  27. S. R. Kulkarni, G. Lugosi, and S. S. Venkatesh, ``Learning Pattern Classification—A Survey,' IEEE Transactions on Information Theory, Special Commemorative Issue 1948-1998, vol. 44, no. 6, pp. 2178-2206, 1998.

  28. S. C. Fang and S. S. Venkatesh, ``Learning Finite Binary Sequences from Half-Space Data,'' Random Structures and Algorithms, vol. 14, pp. 345-381, 1999.

  29. S. S. Venkatesh and J. Ratsaby, ``On Partially Blind Learning Complexity,'' Special Session on Statistical Learning, ISCAS 2000, Geneva, May 28-31, 2000.

  30. Jae H. Song, Santosh S. Venkatesh, Emily. A. Conant, Ted W. Cary, Peter H. Arger, and Chandra M. Sehgal, ``Artificial Neural Network to Aid Differentiation Between Malignant and Benign Breast Masses by Ultrasound Imaging,' Ultrasonic Imaging and Signal Processing, SPIE Conference on Medical Imaging, San Diego, California, February 12--17, 2005.

  31. Jae H. Song, Santosh S. Venkatesh, Emily A. Conant, Peter H. Arger, Chandra M. Sehgal, ``Comparative Analysis of Logistic Regression and Artificial Neural Network for Computer-Aided Diagnosis of Breast Masses,' Academic Radiology, vol.~12, pp.~487--495, 2005.

  32. T. W. Cary, A. Cwanger, S. S. Venkatesh, E. F. Conant, and C. M. Sehgal, ``Comparison of Naïve Bayes and Logistic Regression for Computer-Aided Diagnosis of Breast Masses Using Ultrasound Imaging,'' Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy (eds. Johan G. Bosch and Marvin M. Doyley), Proceedings of SPIE, vol. 8320, pp. 83200M-1 to 83200M-7, 2012.

  33. C. M. Sehgal, T. W. Cary, A. Cwanger, B. Levenback, S. S. Venkatesh, ``Combined Naïve Bayes and Logistic Regression for Quantitative Breast Sonography,'' 2012 IEEE International Ultrasonics Symposium, Dresden, Germany, October 2012.

  34. C. M. Sehgal, L. Sultan, B. Levenback, S. S. Venkatesh, ``Statistical Methods for Breast Mass Classification by Ultrasound Imaging,'' 55th Annual Meeting and Exhibition of the American Association of Physicists in Medicine, Indianapolis, Indiana, August 2013

  35. G. Bouzghar, B. J. Levenback, L. R. Sultan, S. S. Venkatesh, A. Cwanger, E. F. Conant, and C. M. Sehgal, ``Bayesian Probability of Malignancy With Breast Imaging Reporting and Data System Sonographic Features,'' Journal of Ultrasound in Medicine, vol. 33, pp. 641–648, 2014.

  36. L. R. Sultan, G. Bouzghar, B. J. Levenback, N. A. Faizi, S. S. Venkatesh, E. F. Conant, and C. M. Sehgal, ``Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses,'' Advances in Breast Cancer Research, vol. 4, no. 1, 8 pages, January 2015 (http://dx.doi.org/10.4236/abcr.2015.41001).

  37. S. S. Venkatesh, B. J. Levenback, L. R. Sultan, G. Bhouzghar, and C. M. Sehgal, ``Going Beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis Using Adaptive Boosting and Selective Pruning,'' Journal of Ultrasound in Medicine and Biology, vol. 41, issue 12, pp. 3148–3162, December 2015.

  38. L. R. Sultan, S. S. Venkatesh, E. Conant, and C. M. Sehgal, ``Quantitative Doppler Vascularity Improves Computer-Based Sonographic Diagnosis of Breast Cancer,'' Annual Convention of the American Institute of Ultrasound in Medicine (AIUM), Orlando, Florida, March 2017.

  39. A. F. Moustafa, T. W. Cary, L. R. Sultan, S. S. Venkatesh, C. M. Sehgal, ``Color Doppler Ultrasound Improves Performance of Machine Learning Diagnosis of Breast Cancer,'' Diagnostics, vol. 10, pp. 631–646, 2020: doi:10.3390/diagnostics10090631.

  40. L. R. Sultan, T. W. Cary, M. Al-Hasani, M. B. Karmacharya, S. S. Venkatesh, C-A. Assenmacher, E. Radaelli, and C. M. Sehgal, ``Can sequential images from the same object be used for training machine learning models? A case study for detecting liver disease by ultrasound radiomics,'' Artificial Intelligence, vol.~3, pp.~739--750, September 2022: https://doi.org/10.3390/ai3030043.

 

By topic
Communication theory, networks, security

  1. S. B. Bulumulla and S. S. Venkatesh, ``On the Quantized Decorrelating Detector,'' Conference on Information Sciences and Systems, Princeton University, Princeton, New Jersey, March 1996.

  2. S. B. Bulumulla, S. A. Kassam, and S. S. Venkatesh, ``Optimum and Sub-Optimum Receivers for OFDM Signals in Rayleigh Fading Channels,'' Conference on Information Sciences and Systems, Johns Hopkins University, Baltimore, Maryland, March 1997.

  3. S. B. Bulumulla, S. A. Kassam and S. S. Venkatesh, ``An Adaptive, Diversity Receiver for OFDM in Fading Channels,'' International Conference on Communications, Atlanta, GA, 1998.

  4. S. B. Bulumulla, S. A. Kassam and S. S. Venkatesh, ``Pilot Symbol Assisted Diversity Reception for a Fading Channel,'' International Conference on Acoustics, Speech, and Signal Processing, Seattle, WA, 1998.

  5. S. S. Venkatesh, ``CDMA Capacity,'' 2000 Conference on Information Sciences and Systems, Princeton University, March 15-17, 2000.

  6. S. B. Bulumulla, S. A. Kassam, and S. S. Venkatesh, ``Joint Channel Estimation and Detection for OFDM Signals in a Rayleigh Fading Channel,'' IEEE Transactions on Communications, vol. 48, n0. 5, pp. 725–728, May 2000.

  7. C. Gunter, S. Khanna, K. Tan, and S. S. Venkatesh, ``DoS Protection for Reliably Authenticated Broadcast,'' 11th Annual Networkand Distributed System Security Symposium, San Diego, California, February 2004.

  8. S. S. Kunniyur and S. S. Venkatesh, ``Network Devolution and the Growth of Sensory Lacunae in Sensor Networks,'' WiOpt04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, University of Cambridge, UK, March 2004.

  9. S. S. Kunniyur and S. S. Venkatesh, ``Sensor Network Devolution and Breakdown in Survivor Connectivity,'' 2004 International Symposium on Information Theory, Chicago, Illinois, June 27-July 2, 2004.

  10. S. S. Venkatesh, ``Connectivity of Metric Random Graphs,' unpublished notes, 2004.

  11. M. Sherr, M. Greenwald, C. A. Gunter, S. Khanna, and S. S. Venkatesh, ``Mitigating DoS Attack Through Selective Bin Verification,’’ Proceedings of the Workshop on Secure Network Protocols (NPSec), Boston, Massachusetts, November 2005.

  12. S. S. Venkatesh, ``Connectivity, Devolution, and Lacunae in Geometric Random Digraphs,'' Proceedings of the Inaugural Workshop on Information Theory and Applications, San Diego, CA, February 2006.

  13. S. S. Kunniyur and S. S. Venkatesh, ``Threshold Functions, Node Isolation, and Emergent Lacunae in Sensor Networks,' IEEE Transactions on Information Theory, vol.~52, no.~12, pp.~53525372, December 2006.

  14. M. B. Greenwald, S. Khanna, and S. S. Venkatesh, ``How Much Bandwidth Can Attack Bots Commandeer?' Proceedings of the Workshop on Information Theory and Applications, San Diego, CA, January 2007.

  15. S. Khanna, S. S. Venkatesh, O. Fatemieh, F. Khan, and C. A. Gunter, ``Adaptive Selective Verification,'' Proceedings of IEEE INFOCOM 2008, Phoneix, AZ, April 2008.

  16. S. S. Venkatesh, S. Khanna, O. Fatemieh, F. Khan, and C. A. Gunter, ``Nimble Clients Thwart Versatile DDoS Adversaries,'' Information Theory and Applications Workshop, San Diego, CA, 2009.

  17. S. S. Venkatesh, ``A Variation on the Littlewood-Offord Theme with Applications to Phase Transitions in CDMA Detection,'' Information Theory and Applications Workshop, San Diego, CA, 2011.

  18. M. H. R. Khouzani, S. S. Venkatesh, and S. Sarkar, ``Market-Based Control of Epidemics,''  49th Annual Allerton Conference on Communication, Control, and Computing, Allerton Retreat Centre, Monticello, IL, September 2011.

  19. S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh , ``Optimal Patching in Clustered Epidemics of Malware,'' Information Theory and Applications Workshop, San Diego, CA, February 2012.

  20. M. H. R. Khouzani, S. Eshghi, S. Sarkar, S. S. Venkatesh, and N. B. Shroff, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' 13th Annual ACM Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2012), Hilton Head, SC, June 2012.

  21. S. Khanna, S. S. Venkatesh, O. Fatemieh, F. Khan, and C. A. Gunter, ``Adaptive Selective Verification: An Efficient Adaptive Countermeasure to Thwart DoS Attacks,''IEEE Transactions on Networking, vol. 20, issue 3, pp. 715–728, June 2012.

  22. R. Potharaju, E. Hoque, C. Nita-Rotaru, S. Sarkar, and S. S. Venkatesh, ``Closing the Pandora's Box: Defenses for Thwarting Epidemic Outbreaks in Mobile Adhoc Networks,'' 9th IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS 2012), Las Vegas, NV, October 2012.

  23. L. Yan, R. Guerin, K. Hosanagar, Y. Tan, and S. S. Venkatesh, ``Online Social Interactions and Opinion Formation,'' 22nd Annual Workshop on Information Technologies and Systems, Orlando, FL, December 2012.

  24. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,''  Information Theory and Applications Workshop, San Diego, CA, February 2013.

  25. S. S. Venkatesh, ``Opinion Formation in Ising Networks and Some Connections to the Littlewood-Offord Problem,'' Fourth Nordic Workshop on System and Networks Optimization for Wireless (SNOW 2013), Äkäslompolo, Finland, April 2013.

  26. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Spin Glasses with Attitude: Opinion Formation in a Partisan Erdös–Rényi World,'' Information Theory and Applications Workshop, San Diego, CA, February 2014.

  27. E. Hoque, R. Potharaju, C. Nita-Rotaru, S. Sarkar, and S. S. Venkatesh, ``Taming Epidemic Outbreaks in Mobile Ad Hoc Networks,'' Ad Hoc Networks, vol. 24, part A, pp. 57–72, Elsevier, January 2015.

  28. S. Eshghi, S. Sarkar, and S. S. Venkatesh, ``Visibility-Aware Optimal Contagion of Malware Epidemics,'' Information Theory and Applications Workshop, San Diego, CA, February 2015.

  29. S. Eshghi, M. H. R. Khouzani, S. Sarkar, N. B. Shroff, and S. S. Venkatesh, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' IEEE Transactions on Automatic Control, vol. 60, no. 6, pp. 1554–1569, June 2015.

  30. S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh, ``Optimal Patching in Clustered Epidemics of Malware,'' IEEE/ACM Transactions on Networking, vol. 24, no. 1, pp. 283–298, February 2016.

  31. S. Eshghi, S. Sarkar, V. M. Preciado, S. S. Venkatesh, Q. Zhao, R. D'Souza, and A. Swami, ``Spread, then Target, and Advertise in Waves: Optimal Capital Allocation Across Advertising Channels,'' Information Theory and Applications Workshop, San Diego, CA, February 2017.

  32. S. Eshghi, S. Sarkar, and S. S. Venkatesh, ``Visibility-Aware Optimal Contagion of Malware Epidemics,'' IEEE Transactions on Automatic Control, vol. 62, issue 10, pp. 5205–5212, October 2017. Print ISSN: 0018–9286. Online ISSN 1558–2523. Digital Object Identifier: 10.1109/TAC.2016.2632426.

  33. H. Afrasiabi, R. Guerin, and S. S. Venkatesh, ``Opinion Formation in Ising Networks,'' Online Social Networks and Media, vol. 5, pp. 1–22, March 2018.

  34. S. Eshghi, V. Preciado, S. Sarkar, S. S. Venkatesh, Q. Zhao, R. D'Souza, and A. Swami, ``Spread, then Target, and Advertise in Waves: Optimal Budget Allocation Across Advertising Channels,'' Transactions on Network Science and Engineering, Print ISSN: 2327–4697, Online ISSN: 2327–4697, vol. 7, issue 2, October 2, 2018: Digital Object Identifier: 10.1109/TNSE.2018.2873281.

  35. J. Kim, S. Sarkar, S. S. Venkatesh, M. Ryerson, and D. Starobinski, ``Modelling Information Propagation in General V2V-Enabled Transportation Networks,'' Information Theory and Applications Workshop, San Diego, CA, February 2019.

  36. J. Kim, S. Sarkar, S. S. Venkatesh, M. S. Ryerson, and D. Starobinski ``An Epidemiological Diffusion Framework for Vehicular Messaging in General Transportation Networks,'' Transportation Research Part B: Methodological, Special Issue on Innovative Shared Transportation, vol. 131, pp. 160–190, January 2020.

  37. J. Kim, R. Saraogi, S. Sarkar, S. S. Venkatesh, ``Modeling the Impact of Traffic Signals on V2V Information Flow,'' 2020 IEEE 91st Vehicular Technology Conference [online], (VTC2020-Spring).

 

By topic
Epidemic models in malware, contagion, networks & transportation

  1. M. H. R. Khouzani, S. S. Venkatesh, and S. Sarkar, ``Market-Based Control of Epidemics,''  49th Annual Allerton Conference on Communication, Control, and Computing, Allerton Retreat Centre, Monticello, IL, September 2011.

  2. S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh , ``Optimal Patching in Clustered Epidemics of Malware,'' Information Theory and Applications Workshop, San Diego, CA, February 2012.

  3. M. H. R. Khouzani, S. Eshghi, S. Sarkar, S. S. Venkatesh, and N. B. Shroff, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' 13th Annual ACM Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2012), Hilton Head, SC, June 2012.

  4. R. Potharaju, E. Hoque, C. Nita-Rotaru, S. Sarkar, and S. S. Venkatesh, ``Closing the Pandora's Box: Defenses for Thwarting Epidemic Outbreaks in Mobile Adhoc Networks,'' 9th IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS 2012), Las Vegas, NV, October 2012.

  5. E. Hoque, R. Potharaju, C. Nita-Rotaru, S. Sarkar, and S. S. Venkatesh, ``Taming Epidemic Outbreaks in Mobile Ad Hoc Networks,'' Ad Hoc Networks, vol. 24, part A, pp. 57–72, Elsevier, January 2015.

  6. S. Eshghi, S. Sarkar, and S. S. Venkatesh, ``Visibility-Aware Optimal Contagion of Malware Epidemics,'' Information Theory and Applications Workshop, San Diego, CA, February 2015.

  7. S. Eshghi, M. H. R. Khouzani, S. Sarkar, N. B. Shroff, and S. S. Venkatesh, ``Optimal Energy-Aware Epidemic Routing in DTNs,'' IEEE Transactions on Automatic Control, vol. 60, no. 6, pp. 1554–1569, June 2015.

  8. S. Eshghi, M. H. R. Khouzani, S. Sarkar, and S. S. Venkatesh, ``Optimal Patching in Clustered Epidemics of Malware,'' IEEE/ACM Transactions on Networking, vol. 24, no. 1, pp. 283–298, February 2016.

  9. S. Eshghi, S. Sarkar, V. M. Preciado, S. S. Venkatesh, Q. Zhao, R. D'Souza, and A. Swami, ``Spread, then Target, and Advertise in Waves: Optimal Capital Allocation Across Advertising Channels,'' Information Theory and Applications Workshop, San Diego, CA, February 2017.

  10. S. Eshghi, S. Sarkar, and S. S. Venkatesh, ``Visibility-Aware Optimal Contagion of Malware Epidemics,'' IEEE Transactions on Automatic Control, vol. 62, issue 10, pp. 5205–5212, October 2017. Print ISSN: 0018–9286. Online ISSN 1558–2523. Digital Object Identifier: 10.1109/TAC.2016.2632426.

  11. S. Eshghi, V. Preciado, S. Sarkar, S. S. Venkatesh, Q. Zhao, R. D'Souza, and A. Swami, ``Spread, then Target, and Advertise in Waves: Optimal Budget Allocation Across Advertising Channels,'' Transactions on Network Science and Engineering, Print ISSN: 2327–4697, Online ISSN: 2327–4697, vol. 7, issue 2, October 2, 2018: Digital Object Identifier: 10.1109/TNSE.2018.2873281.

  12. J. Kim, S. Sarkar, S. S. Venkatesh, M. Ryerson, and D. Starobinski, ``Modelling Information Propagation in General V2V-Enabled Transportation Networks,'' Information Theory and Applications Workshop, San Diego, CA, February 2019.

  13. J. Kim, S. Sarkar, S. S. Venkatesh, M. S. Ryerson, and D. Starobinski ``An Epidemiological Diffusion Framework for Vehicular Messaging in General Transportation Networks,'' Transportation Research Part B: Methodological, Special Issue on Innovative Shared Transportation, vol. 131, pp. 160–190, January 2020.

  14. J. Kim, R. Saraogi, S. Sarkar, S. S. Venkatesh, ``Modeling the Impact of Traffic Signals on V2V Information Flow,'' 2020 IEEE 91st Vehicular Technology Conference [online], (VTC2020-Spring).

 
 

By topic
AI in cancer diagnosis

  1. Jae H. Song, Santosh S. Venkatesh, Emily. A. Conant, Ted W. Cary, Peter H. Arger, and Chandra M. Sehgal, ``Artificial Neural Network to Aid Differentiation Between Malignant and Benign Breast Masses by Ultrasound Imaging,' Ultrasonic Imaging and Signal Processing, SPIE Conference on Medical Imaging, San Diego, California, February 12--17, 2005.

  2. Jae H. Song, Santosh S. Venkatesh, Emily A. Conant, Peter H. Arger, Chandra M. Sehgal, ``Comparative Analysis of Logistic Regression and Artificial Neural Network for Computer-Aided Diagnosis of Breast Masses,' Academic Radiology, vol.~12, pp.~487--495, 2005.

  3. T. W. Cary, A. Cwanger, S. S. Venkatesh, E. F. Conant, and C. M. Sehgal, ``Comparison of Naïve Bayes and Logistic Regression for Computer-Aided Diagnosis of Breast Masses Using Ultrasound Imaging,'' Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy (eds. Johan G. Bosch and Marvin M. Doyley), Proceedings of SPIE, vol. 8320, pp. 83200M-1 to 83200M-7, 2012.

  4. C. M. Sehgal, T. W. Cary, A. Cwanger, B. Levenback, S. S. Venkatesh, ``Combined Naïve Bayes and Logistic Regression for Quantitative Breast Sonography,'' 2012 IEEE International Ultrasonics Symposium, Dresden, Germany, October 2012.

  5. C. M. Sehgal, L. Sultan, B. Levenback, S. S. Venkatesh, ``Statistical Methods for Breast Mass Classification by Ultrasound Imaging,'' 55th Annual Meeting and Exhibition of the American Association of Physicists in Medicine, Indianapolis, Indiana, August 2013

  6. G. Bouzghar, B. J. Levenback, L. R. Sultan, S. S. Venkatesh, A. Cwanger, E. F. Conant, and C. M. Sehgal, ``Bayesian Probability of Malignancy With Breast Imaging Reporting and Data System Sonographic Features,'' Journal of Ultrasound in Medicine, vol. 33, pp. 641–648, 2014.

  7. L. R. Sultan, G. Bouzghar, B. J. Levenback, N. A. Faizi, S. S. Venkatesh, E. F. Conant, and C. M. Sehgal, ``Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses,'' Advances in Breast Cancer Research, vol. 4, no. 1, 8 pages, January 2015 (http://dx.doi.org/10.4236/abcr.2015.41001).

  8. S. S. Venkatesh, B. J. Levenback, L. R. Sultan, G. Bhouzghar, and C. M. Sehgal, ``Going Beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis Using Adaptive Boosting and Selective Pruning,'' Journal of Ultrasound in Medicine and Biology, vol. 41, issue 12, pp. 3148–3162, December 2015.

  9. L. R. Sultan, S. S. Venkatesh, E. Conant, and C. M. Sehgal, ``Quantitative Doppler Vascularity Improves Computer-Based Sonographic Diagnosis of Breast Cancer,'' Annual Convention of the American Institute of Ultrasound in Medicine (AIUM), Orlando, Florida, March 2017.

  10. A. F. Moustafa, T. W. Cary, L. R. Sultan, S. S. Venkatesh, C. M. Sehgal, ``Color Doppler Ultrasound Improves Performance of Machine Learning Diagnosis of Breast Cancer,'' Diagnostics, vol. 10, pp. 631–646, 2020: doi:10.3390/diagnostics10090631.

  11. C. M. Sehgal, S. S. Venkatesh, and L. M. Sultan, ``Machine Implemented Methods, Systems, and Apparatuses for Improving Diagnostic Performance​’’, United States Patent:  US 11,071,517 B2, published July 27, 2021.

  12. L. R. Sultan, T. W. Cary, M. Al-Hasani, M. B. Karmacharya, S. S. Venkatesh, C-A. Assenmacher, E. Radaelli, and C. M. Sehgal, ``Can sequential images from the same object be used for training machine learning models? A case study for detecting liver disease by ultrasound radiomics​,'' Artificial Intelligence, vol.~3, pp.~739--750, September 2022: https://doi.org/10.3390/ai3030043.

 

By topic
Optical signal processing

  1. D. Psaltis, E. G. Paek, and S. S. Venkatesh, ``Acousto-Optic/CCD Image Processor,'' Proceedings of the International Optical Computing Conference, MIT, Cambridge, Massachusetts, pp. 204-208, 1983.

  2. D. Psaltis, S. S. Venkatesh, and E. G. Paek, ``Optical Image Correlation with a Binary Spatial Light Modulator,'' Optical Engineering, vol. 23, no. 6, pp. 698-704, 1984.

  3. D. Psaltis, J. Hong, and S. S. Venkatesh, ``Shift-Invariance in Optical Associative Memories,'' in Proceedings of SPIE: First Annual Symposium on Optoelectronics and Laser Applications, Los Angeles, California, 1986.

  4. S. S. Venkatesh and D. Psaltis, ``Binary Filters for Pattern Classification,'' IEEE Transactions on Acoustics, Speech, Signal Processing, vol. ASSP-37, pp. 604-611, 1989.

 

By topic
Miscellany

  1. B. Sarath and S. S. Venkatesh, ``Auditor Liability for Management Fraud,'' Fifth Annual Conference on Intelligent Systems in Accounting, Finance, and Management, Palo Alto, California, November 1993.

  2. S. S. Venkatesh, ``Remarks at Commencement by the Chair of the Faculty Senate: The Continuing Quest for Knowledge,'' The Almanac of the University of Pennsylvania, vol. 63, issue 35, Supplement p. V, May 23, 2017.

  3. S. S. Venkatesh, ``Welcome Back From the Senate Chair: Knowledge as a Beacon,’' The Almanac of the University of Pennsylvania, vol. 64, issue 2, August 29, 2017.

  4. L. W. Perna, S. S. Venkatesh, and J. A. Pinto-Martin, ``Five Tax Changes that will Hurt U.S. Higher Education: A Letter to Members of Congress from the Leaders of Penn's Faculty Senate,'' Medium, December 6, 2017: https://medium.com/@lauraperna1/five-tax-changes-that-will-hurt-u-s-higher-education-239072371409.

  5. J. A. Pinto-Martin, S. S. Venkatesh, and L. W. Perna, ``2018 Penn Teach-In: The Production, Dissemination, and Use of Knowledge, March 18–22,'' The Almanac of the University of Pennsylvania, volume 64, issue 26, March 13, 2018.

  6. S. S. Venkatesh, ``Report of the Chair of the Faculty Senate,’' The Almanac of the University of Pennsylvania: Supplements, vol. 64, issue 34, May 8, 2018.

  7. V. Amelkin, R. Vohra, and S. S. Venkatesh, ``Structure and Dynamics of Contagion in Financial Networks with Implications for Systemic Risk,''  Sixth Annual Conference on Network Science in Economics, Chicago, IL, March 2021.

  8. V. Amelkin, R. Vohra, and S. S. Venkatesh, ``Contagion and Equilibria in Diversified Financial Networks,'' Economic Theory Conference VI, Becker—Friedman Institute, University of Chicago, Chicago, IL, August 2021.

  9. V. Amelkin, S. S. Venkatesh, and R. Vohra, ``Contagion and Equilibria in Diversified Financial Networks,'' Econometrica, submitted November 2021.

  10. A. DasGupta, T. M. Sellke, and S. S. Venkatesh,  ``The Exact and Asymptotic Distributions of X mod k and Connections to Tauberian Theorems, Congruence Algebra, and Fourier Analysis,'’ unpublished, 2021.

  11. K. Dasaratha, S. S. Venkatesh, and R. Vohra, ``Financial Contagion in Stochastic Block Graphons'', Seventh Annual Conference on Network Sciences and Economics, Booth School of Business, University of Chicago, Chicago, IL, March 18–20, 2022.

  12. S. S. Venkatesh, ``Clarity Amid Catastrophe — Understanding Chance'', TEDx Penn 2022: ÆFFECT, University of Pennsylvania, Philadelphia, PA, April 16, 2022.

  13. S. S. Venkatesh, ``From the Sad Story of a Birthday Cake to Social Distancing, Political Turmoil, and Pandemic,'' ASIME Keynote, Adelphi University, New York, July 7, 2022.