Past talks

Spring 2021 Talks

Talk recordings from Spring 2021 are available here.

Jan 29 Lin Chen (UC Berkeley) Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS details
Feb 12 Michał Dereziński (UC Berkeley) Bridging algorithmic and statistical randomness in machine learning details
Feb 19 Lin F. Yang (UCLA) Efficient methods for reinforcement learning with general function approximation details
Feb 26 Song Mei (UC Berkeley) The efficiency of kernel methods on structured datasets details
Mar 5 Chi Jin (Princeton) Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms details
Mar 12 R Srikant (UIUC) Sample Complexity and Overparameterization Bounds for Neural Temporal Difference Learning details
Mar 19 Alex Dimakis (UT Austin) Deep Generative models and Inverse Problems details
Mar 26 Spring break
Apr 2 Miaoyan Wang (UW Madison) Beyond the Signs: Nonparametric Tensor Completion via Sign Series details
Apr 9 Nhat Ho (UT Austin) Instability, Computational Efficiency and Statistical Accuracy details
Apr 16 Aarti Singh (CMU) Learning from preferences and labels details
Apr 23 Salman Avastimehr (USC) Trustworthy and Scalable Federated Learning details
Apr 30 Nihar Shah (CMU) Two F-words in Peer Review (Fraud and Feedback) details
May 7 Jiaming Xu (Duke) The planted matching problem: Sharp threshold and infinite-order phase transition details
May 14 Alekh Agarwal (Microsoft Research) Towards a Theory of Representation Learning for Reinforcement Learning details

Fall 2020 Talks

Talk recordings from Fall 2020 are available here.

Sep 11 Sam Hopkins (UC Berkeley) Recent Advances in Algorithmic Heavy-Tailed Statistics details
Sep 18 Yuanzhi Li (CMU) Backward Feature Correction: How can Deep Learning perform Deep Learning? details
Sep 25 Steve Hanneke (TTIC) Multi-task Learning: Optimal Rates and a No-Free-Lunch Theorem details
Oct 2 Haipeng Luo (USC) From bandits to MDPs: optimally and adaptively learning episodic MDPs with adversarial losses details
Oct 9 Wen Sun (Cornell) Exploration and Robustness in Policy Gradient Learning details
Oct 16 Dimitris Papailiopoulos (Wisconsin-Madison) Learning is Pruning details
Oct 19 Jerry Li (Microsoft) Faster and Simpler Algorithms for List Learning details
Oct 23 Pulkit Grover (CMU) Measures of Information Flows in Natural and Artificial Computational Systems details
Oct 30 Sharon Li (Wisconsin-Madison) Reliable Open-World Learning Against Out-of-distribution Data details
Nov 6 Cong Ma (UC Berkeley) Bridging convex and nonconvex optimization in noisy matrix completion: Stability and uncertainty quantification details
Nov 13 Kangwook Lee (UW Madison) Make-or-break issues in fair classification details
Nov 20 Zhizhen Zhao (UIUC) Exploiting Group and Geometric Structures for Massive Data Analysis details
Dec 4 Aaron Wagner (Cornell) What Hockey Teams and Foraging Animals Can Teach Us About Feedback Communication details
Dec 11 Anru Zhang (Wisconsin-Madison) Importance Sketching for Fast Low-rank Matrix/Tensor Learning: Algorithm and High-order Convergence details

Spring 2020 Talks

Feb 11 Siva Theja Maguluri (Georgia Tech) Finite Sample Convergence Bounds of Off-Policy Reinforcement Learning Algorithms details
Feb 12 Krishnakumar Balasubramanian (UC Davis) Normal Approximations for Stochastic Iterative Estimators (and Martingales) details
Feb 19 Alon Kipnis (Stanford) Two-sample Problem for High-Dimensional Multinomials and Testing Authorship details
Feb 26 David Tse (Stanford) Blockchains, branching random walks and the number e details
Feb 28 Aly El Gamal (Purdue) Information Theory and Deep Learning for Future Wireless Networks details
Mar 4 David Hong (UPenn) Understanding Parallel Analysis Methods for Rank Selection in PCA details
Rest of the talks were cancelled due to COVID-19 outbreak.

Fall 2019

Aug 28 Ramya Vinayak (UW Seattle) Learning from Sparse Data details
Sep 18 Arya Mazumdar (UMass) Sample complexity of mixture of sparse linear regressions details
Sep 25 Feng Ruan (UC Berkeley) Searching for Interactions in Linear Time details
Oct 2 Amir Gholami (UC Berkeley) Systematic Quantization of Neural Networks Through Second-Order Information details
Oct 9 Nhat Ho (UC Berkeley) Statistical and computational perspective of mixture and hierarchical models details
Oct 18 Jiaming Xu (Duke) Spectral graph matching and regularized quadratic relaxations details
Oct 30 Cheuk Ting Li (UC Berkeley) One-shot Information Theory via Poisson Processes details
Nov 6 Yuejie Chi (CMU) Distributed Stochastic Optimization with Variance Reduction and Gradient Tracking details
Nov 13 Guy Bresler (MIT) Towards an Average-case Complexity of High-dimensional Statistics details
Nov 20 Victoria Kostina (Caltech) Towards a Theory of Information for Dynamical Systems details
Dec 4 Animesh Kumar (IIT Bombay) On sampling and inference of spatial fields from samples taken by a location-unaware mobile sensor details
Dec 6 Jingbo Liu (MIT) Gaussian limits in two inference problems details

Spring 2019

Feb 4 Clement Canonne (Stanford) Statistical Inference Under Local Information Constraints details
Feb 25 Gautam Kamath (Simons) Privately Learning High-Dimensional Distributions details
Mar 4 Yuandong Tian (Facebook AI) Reproducing AlphaZero: what we learn details
Mar 11 Lalitha Sankar (Arizona State) Information-theoretic Privacy: Leakage, robustness, and mechanism design details
Mar 15 Deniz Gunduz (Imperial College London) Learn to Communicate - Communicate to Learn details
Mar 20 Elisa Celis (EPFL) Fairness in Machine Learning for Online Social Systems details
Apr 1 Aaditya Ramdas (CMU) On the bias, risk and consistency of sample means in multi-armed bandits details
Apr 8 Piyush Srivastava (TIFR) Structure recovery in graphical models details
Apr 15 Steven Wu (U. Minnesota) How to Use Heuristics for Differential Privacy details
Apr 24 Farzan Farnia (Stanford) A Convex Duality Framework for GANs details
Apr 29 Katrina Ligett (HUJI) A necessary and sufficient stability notion for adaptive generalization details
May 6 Anand Sarwate (Rutgers) Between Shannon and Hamming: how bad can the channel be? details

Fall 2018

Aug 27 Jingbo Liu (MIT) Reverse hypercontractivity beats measure concentration for information theoretic converses details
Sep 5 Lav Varshney (UIUC) Information-Theoretic Approaches to Clustering and Interpretable Concept Learning details
Sep 10 Maryam Fazel (UW Seattle) Online Competitive Algorithms for Resource Allocation details
Oct 2 Yan Shuo Tan Efficient algorithms for phase retrieval in high dimension details
Oct 10 Uzi Pereg (Technion) Arbitrarily Varying Broadcast and Relay Channels details
Oct 15 Ruoyu Sun (UIUC) Understanding Landscape of Neural Networks for Binary Classification details
Oct 26 Nihar Shah (CMU) Battling Demons in Peer Review details
Oct 29 Deanna Needell (UCLA) Iterative projective approaches for inconsistent and massively corrupted systems details
Nov 1 Yingbin Liang (Ohio State) SGD Converges to Global Minimum in Deep Learning via Star-Convex Path details
Nov 6 Laura Balzano (Michigan) Non-linear matrix completion details
Nov 14 Henk Wymeersch (Chalmers) 5G Positioning for Connected Vehicles details
Nov 26 Mary Wootters (Stanford) Two Stories about Group Testing details
Dec 3 Sreeram Kannan (UW Seattle) Deconstructing the Blockchain to approach physical limits details
Dec 10 Yuval Cassuto (Technion) Beyond Repair: Codes with Random Access details

Spring 2018

Jan 26 Sivan Toledo (Tel-Aviv) The ATLAS Reverse-GPS System: High-Throughput Wildlife Tracking details
Feb 7 Raymond Yeung (CUHK) Shannon's Information Measures and Markov Structures details
Feb 22 Yuantao Gu (Tsinghua) RIP of Random Projection for Low-Dimensional Subspaces details
Mar 9 John Wright (Columbia) Nonconvex Sparse Deconvolution: Geometry and Efficient Methods details
Mar 12 Reinhard Heckel (Rice) Robust Storage of Information in DNA Molecules details
Mar 19 R. Srikant (UIUC) Queues, Balls and Bins, and Association details
Mar 23 Angie Wang (Berkeley) An Agile Approach to FFTs and Hardware DSP Generation details
Apr 2 Balaji Prabhakar (Stanford) Self-Programming Networks: Architecture and Algorithms details
Apr 9 Bruce Hajek (UIUC) Gene regulatory network reconstruction from high throughput sequencing data details
Apr 12 Jun Chen (McMaster) From Gaussian Multiterminal Source Coding to DistributedKarhunen–Loève Transform details
Apr 16 Sid Banerjee (Cornell) Allocating Resources, in the Future details
Apr 23 Inderjit Dhillon (A9) Stabilizing Gradients for Deep Neural Networks details
Apr 30 Leo Miolane (INRIA) Phase transitions in generalized linear models details
May 4 Fanny Yang (Berkeley) Computational guarantees for statistical learning algorithms details
May 7 Eva Tardos (Cornell) Learning with Low Approximate Regret with Partial Feedback details

Fall 2017

Sep 25 Stephen Wright (UW Madison) Algorithmic Tools for Smooth Nonconvex Optimization details
Oct 2 Stefanie Jegelka (MIT) Submodularity and Optimal Transport in ML details
Oct 4 Gerald Friedland (Berkeley) Capacity Scaling of Neural Networks details
Oct 9 Zeyuan Allen-Zhu (Microsoft Research) Optimal Experimental Design via Regret Minimization details
Oct 16 Yin Tat Lee (U. Washington) Kannan-Lovasz-Simonovitz (KLS) conjecture details
Oct 23 Sebastien Bubeck (Microsoft Research) Sparsity, variance, and curvature in multi-armed bandits details
Oct 30 Ludwig Schmidt (MIT) Efficiently Optimizing over (Non-Convex) Cones Using Approximate Projections details
Nov 6 Jiantao Jiao (Stanford) Modern Estimation of Information Theoretic Functionals details
Nov 8 Mina Karzand (MIT) Regret bounds for a latent variable recommendation systems model details
Nov 13 Lieven Vandenberghe (UCLA) Primal-dual first-order methods for convex optimization details
Nov 20 Chi Jin (Berkeley) How to Escape Saddle Points Efficiently details
Nov 22 Shusen Wang (Berkeley) Communication-Efficient Distributed Optimization via a 2nd-Order Method details
Nov 27 Philipp Walk (Caltech) Blind Deconvolution Methods for Short Message Communications details
Dec 11 Marco Mondelli (Stanford) Fundamental Limits of Weak Recovery with Applications to Phase Retrieval details

Spring 2017

Jan 30 Vincent Poor (Princeton) Network Analysis Problems Motivated by the Smart Grid details
Feb 6 Victoria Kostina (Caltech) Information-theoretic tradeoffs in control details
Feb 8 Ziv Goldfeld (Ben-Gurion) Semantic security versus active adversaries details
Feb 13 Reinhard Heckel (Berkeley) Active Ranking from Pairwise Comparisons details
Feb 21 Ilan Shomorony (Berkeley) DNA Sequencing: From Information Limits to Assembly Software details
Feb 27 Haim Permuter (Ben-Gurion) Causality and directed information details
Mar 6 Lele Wang (Stanford) Graph information ratio details
Mar 13 Matus Telgarsky (UIUC) Logistic regression convergence rates details
Mar 17 Yuval Cassuto (Technion) Coded Network Switches details
Mar 20 Aditya Mahajan (McGill) Fundamental limits of remote estimation details
Mar 30 Rob Nowak (UW Madison) Ordinal Embedding details
Apr 3 Amin Karbasi (Yale) Cracking Big Data with Small Data details
Apr 6 Anthony Quinn (Dublin/Berkeley) Fully Probabilistic Design for External Stochastic Knowledge Processing details
Apr 10 Aaditya Ramdas (Berkeley) Decentralized decision making on networks with false discovery rate control details
Apr 17 Ayfer Ozgur (Stanford) Cover’s Open Problem: “Capacity of the Relay Channel” details
Apr 20 Carlee Joe-Wong (CMU) Letting Go of Network Neutrality details
Apr 24 Suvrit Sra (MIT) Geometric optimization: convex and nonconvex details
May 8 Nihar Shah (Berkeley) Learning from People details
May 26 Shlomo Shamai (Technion) Information-Estimation Relations in Gaussian Networks details

Fall 2016

Aug 22 Shashanka Ubaru (UMN) Codes for low-rank approximation and group testing details
Aug 29 Carl Simon-Gabriel (Tubingen) Kernel Mean Embeddings: A Quick Guided Tour details
Aug 31 Rashmi KV (Berkeley) Erasure Coding for Big-data Systems details
Sep 6 Michael Gastpar (EPFL) Towards an Algebraic Network Information Theory details
Sep 16 Animesh Kumar (IIT-B) Bandlimited Estimation by Location-Unaware Sensors details
Sep 19 Ilan Ben Bassat (Tel Aviv) Hashing and Sampling for Genome Assembly details
Oct 10 Ramya Srinivasan (Intel) Beyond GPS: high accuracy smartphone technologies details
Oct 17 Ahmed El Alaoui (Berkeley) Decoding from Random Histogram Queries details
Oct 24 Somayeh Sojoudi (Berkeley) Data-driven sparse network estimation details
Oct 31 Lifeng Lai (Davis) Distributed Inference with Compressed Data details
Nov 4 Yue Lu (Harvard) Phase Transitions in Stochastic Optimization details
Nov 14 Jingge Zhu (Berkeley) Structured Codes in Multi-user Communication details

Spring 2016

Jan 25 James Zou (MSR/ MIT) details
Feb 22 Xiugang Wu (Stanford) details
Feb 29 Idoia Ochoa (Stanford) details
Mar 1 Julien Mairal (INRIA) details
Mar 16 Marco Cuturi (Kyoto) details
Apr 11 Aditya Ramamoorthy (Iowa state) details
Apr 18 Dimitris Papailiopoulos (Berkeley) details

Older talks

Visit the (no longer maintained) NCD seminar webpage.