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Salim El Rouayheb - Shannon Channel
This channel will feature a series of online talks related to the general area of information theory.
Latest talk
Stay tuned:
List of the past talks:
S04E05: Michael Gastpar, Ecole Polytechnique Fédérale de Lausanne (EPFL), Common Information Components Analysis, March 31, 2021.
S04E04: Aaron B. Wagner, Cornell University, What Hockey Teams and Foraging Animals Can Teach Us About Feedback Communication, November 18, 2020 and November 20, 2020 (slides part 1 and part 2).
S04E03: Yihong Wu, Yale University, Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models, October 30, 2020.
S04E02: Xiugang Wu, University of Delaware, Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover, September 25, 2020.
S04E01: Hamed Hassani, University of Pennsylvania, Two Facets of Learning Robust Models: Fundamental Limits and Generalization to Natural Out-of-Distribution Inputs, September 4, 2020.
S03E17: Rashmi Vinayak, Carnegie Mellon University, Convertible Codes: A New Class of Codes for Efficient Conversion of Coded Data in Distributed Storage, June 19, 2020.
S03E16: Derya Malak, Rensselaer Polytechnic Institute, Coding and Sampling for Delay, June 12, 2020.
S03E15: Ayfer Ozgur, Stanford University, Distributed Estimation under Communication and Privacy Constraints, June 5, 2020.
S03E14: Nihar Shah, Carnegie Mellon University, The one with Nihar Shah talking about Peer Review, May 29, 2020.
S03E13: Anant Sahai, , UC Berkeley, Interpolation in learning: steps towards understanding when overparameterization is harmless, when it helps, and when it causes harm (Part II), May 27, 2020.
S03E12: Anant Sahai, UC Berkeley, Interpolation in learning: steps towards understanding when overparameterization is harmless, when it helps, and when it causes harm (Part I), May 22, 2020.
S03E11: Babak Hassibi, Rutgers University, The Blind Men and the Elephant: The Mysteries of Deep Learning, May 15, 2020.
S03E10: Min Xu, Rutgers University, Inference for the History of a Randomly Growing Tree, May 8, 2020.
S03E09: Hessam Mahdavifar, University of Michigan, New Faces of Channel Coding: 5G and Beyond, April 24, 2020.
S03E08: Roy Yates, Rutgers University, The Age of Information in Networks: Moments, Distributions, and Sampling, April 17, 2020.
S03E07: Sidharth Jaggi, Chinese University of Hong Kong, Shannon-Style Theorems for Adversarial Channels: When Can One Pack (Exponentially) Many Copies of a Given Pattern?, April 3, 2020.
S03E06: Maxim Raginsky, University of Illinois at Urbana-Champaign, Information-Theoretic Lower Bounds for Distributed Function Computation, February 28, 2020.
S03E05: Vijay Subramanian, University of Michigan, A Structural Result for Personalized PageRank and its Algorithmic Consequences, January 31, 2020. (Slides)
S03E04: Flavio du Pin Calmon, Harvard University, On Representations and Fairness: Information-theoretic Tools for Machine Learning, November 22, 2019.
S03E03: Ali Tajer, Rensselaer Polytechnic Institute, Active Sensing for Quickest Event Detection in Networks, October 25, 2019.
S03E02: Matthieu Bloch, Georgia Institute of Technology, Towards Undetectable Quantum Communications, October 11, 2019.
S03E01: Joao Ribeiro, Imperial College London, Coded and Uncoded Trace Reconstruction, September 27, 2019. (Slides)
S02E14: Victoria Kostina, California Institute of Technology, Towards a Theory of Information for Dynamical Systems, Friday, June 7, 2019.
S02E13: Hoon Cho, Massachusetts Institute of Technology, Biomedical Data Sharing and Analysis With Privacy, Friday, May 10, 2019.
S02E12: Denali Molitor, University of California, Los Angeles, Randomized Kaczmarz with Averaging, Friday, April 5, 2019. (Slides)
S02E11: Murat Kocaoglu, MIT-IBM Watson AI Lab, Entropic Methods for Causal Discovery, Friday, March 1, 2019.
S02E10: Gauri Joshi, Carnegie Mellon University, Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD, Friday, February 1, 2019. (Slides)
S02E09: Arya Mazumdar, University of Massachusetts Amherst, Graph Clustering: Variations on the Block Models, Friday, December 7, 2018.
S02E08: Ananda Theertha Suresh, Google Research, cpSGD: Communication-efficient and differentially-private distributed SGD, Friday, November 16, 2018. (Slides)
S02E07: Himanshu Tyagi, Indian Institute of Science, Inference under Local Information Constraints, Friday, November 2, 2018.
S02E06: Po-Ling Loh, University of Wisconsin-Madison, Statistical Inference for Infectious Disease Modeling, Thursday, June 14, 2018.
S02E05: Varun Jog, University of Wisconsin-Madison, Information-theoretic Perspectives on Learning Algorithms, Tuesday, May 8, 2018. (Slides)
S02E04: Mary Wootters, New Tricks for Old Codes: Regenerating Codes Edition , Monday, April 23, 2018.
S02E03: Jayadev Acharya, Estimating Symmetric Properties of Distributions: Maximum Likelihood Strikes Back!, Monday, March 5, 2018. (Slides)
S02E02: Ravi Tandon, Communication-efficient Distributed Learning: Information-theoretic Perspectives & Tradeoffs, Monday, February 19, 2018.
S02E01: Bobak Nazer, Towards an Algebraic Network Information Theory, Tuesday, June 13, 2017.
S01E06: Anand Sarwate, From Local to Distributed Differential Privacy, Tuesday, April 26, 2016.
S01E05: Dimitris Papailiopoulos, Less Talking More Learning: Avoiding Coordination In Parallel Machine Learning Algorithms, Wednesday, April 20, 2016.
S01E04: Ilan Shomorony, The DNA Assembly Problem: Designing algorithms based on Information Limits, Monday, March 28, 2016.
S01E03: Pulkit Grover, Error-correction and Suppression in Communication and Computing: a Tradeoff Between Information and Energy Dissipation, Monday, November 23, 2015.
S01E02: Yonatan Kaspi, Searching with Measurement Dependent Noise, Monday, July 13, 2015.
S01E01: Alex Sprintson, Secure Network Coding Schemes for Wireless and Data Storage Networks - Part 2, Wednesday, June 3, 2015.
Pilot: Alex Sprintson, Secure Network Coding Schemes for Wireless and Data Storage Networks - Part 1, Wednesday, May 27, 2015.
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