Talks

11-Apr Marco Mondelli IST Austria

Optimization, Robustness and Attention in Deep Learning: Insights from Random and NTK Feature Models

18-Apr Nikolai Matni University of Pennsylvania

What makes learning to control easy or hard?

25-Apr Eric Mazumdar Caltech

Challenges & Opportunities for Learning in Strategic Environments

23-May Vijay Vazirani UC Irvine

25-Jan Anant Sahai UC Berkeley

Asymptotic Learning in Overparameterized Models

08-Feb Dongning Guo Northwestern University

Permissionless Blockchains: Fundamental Trade-offs in Throughput, Latency, and Safety

15-Feb Johan Ugander Stanford

Clustering for causal inference under network interference

22-Feb Venkatesan Guruswami UC Berkeley

Solving semirandom planted CSPs via SDP-certificates and spectral sparsification

29-Feb Ellen Vitercik Stanford

From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection

07-Mar Preetum Nakkiran Apple Research

What Algorithms can Transformers Learn? A Study in Length Generalization

14-Mar Chinmay Maheshwari UC Berkeley

Design and Analysis of Decentralized Learning Enabled Multi-Agent Systems

12-Oct Ernest Ryu Seoul National University

Toward a grand unified theory of accelerations in optimization and machine learning

19-Oct Noam Brown OpenAI

CICERO: Human-Level Performance in the Game of Diplomacy by Combining Language Models with Strategic Reasoning

26-Oct Qijia Jiang UC Berkeley

From Optimization to Sampling, with a touch of Schrödinger Bridge

02-Nov Chai Toh UC Berkeley

See versus Hear - Wireless Digital Traffic Signs

09-Nov Ajil Jalal UC Berkeley

Compressed Sensing using Generative Models: Theory and Applications

16-Nov Gordon Wetzstein Stanford

Efficient Neural Scene Representation, Rendering, and Generation

30-Nov Ciamac Moallemi Columbia University

The Economics of Automated Market Making and Decentralized Exchanges

07-Dec Michael Mahoney ICSI, LBNL, and University of California, Berkeley

Model Selection And Ensembling When There Are More Parameters Than Data

06-Apr Yuxin Chen University of Pennsylvania

Breaking the Sample Size Barrier in Reinforcement Learning

13-Apr Jiantao Jiao UC Berkeley

Near-Optimal Algorithms for Imitation Learning

21-Apr Jelani Nelson UC Berkeley

New local differentially private protocols for frequency and mean estimation

27-Apr Ashish Goel Stanford

Representative and Deliberative Social Choice

18-May Laurent Lessard Northeastern University

Automating the analysis and design of iterative optimization algorithms

25-May Shirin Jalali Rutgers University

Mathematical underpinnings of emerging computational imaging inverse problems

31-May Yair Carmon Tel Aviv University

DoG is SGD’s best friend: toward tuning-free stochastic optimization

01-Jun Madeleine Udell Stanford University

Low rank approximation for faster optimization

12-Jan John Cioffi Stanford University

Next-Generation Communication/Artificial-Intelligence Challenges

19-Jan Krishna Balasubramanian UC Davis

Recent Advances in Non-log-concave and Heavy-tailed Sampling

26-Jan Lizhong Zheng MIT

Learning with Feature Geometry

02-Feb Vasilis Syrgkanis Stanford

Adversarial machine learning and instrumental variables for flexible causal modeling

23-Feb Ziv Goldfeld Cornell University

Gromov-Wasserstein Distances: Entropic Regularization, Duality, and Sample Complexity

03-Mar Caroline Uhler MIT

Causal Representation Learning – A Proposal

09-Mar Lester Mackey Microsoft Research

Kernel Thinning and Stein Thinning

16-Mar Ilan Shomorony UIUC

An Information Theory for Out-of-Order Information: Applications in DNA Data Storage and Beyond

23-Mar Nika Haghtalab UC Berkeley

Looking beyond the Worst-Case Adversaries in Machine Learning

29-Sep Nicholas Carlini Google Brain

Attacking the privacy of machine learning models

06-Oct Theodor Misiakiewicz Stanford

Computational aspects of learning sparse functions with neural networks

13-Oct Courtney Paquette McGill University

Stochastic Algorithms in the Large: Batch Size Saturation, Stepsize Criticality, Generalization Performance, and Exact Dynamics

20-Oct Ludwig Schmidt University of Washington

A data-centric view on reliable generalization

27-Oct Kevin Jamieson University of Washington

Towards Instance-Optimal Algorithms for Reinforcement Learning

03-Nov Damek Davis Cornell

Leveraging 'partial' smoothness for faster convergence in nonsmooth optimization

10-Nov Jason R. Marden UCSB

Recent Advances in Colonel Blotto Games and the Connection to Controls

17-Nov Raaz Dwivedi Harvard/MIT

Two Vignettes on Efficient Procedures for Personalized Decision Making

01-Dec Yuchen Wu Stanford

Fundamental limits of low-rank matrix estimation with diverging aspect ratios

08-Dec Lior Pachter Caltech

What kind of information is there in single-cell genomics data?

07-Apr Damek Davis Cornell

[POSTPONED to Fall] TBD

14-Apr Sham Kakade Harvard

What is the Statistical Complexity of Reinforcement Learning?

21-Apr Ben Recht UC Berkeley

Historical thoughts on modern prediction

28-Apr Niladri Chatterji Stanford

Two vignettes about interpolation and generalization in overparameterized models

05-May Elad Hazan Princeton

The online convex optimization approach to control

12-May Amit Sahai UCLA

Beyond the Csiszár-Körner Bound: Best-Possible Wiretap Coding via Obfuscation

19-May Vidya Muthukumar Georgia Tech

Algorithms and computational limits for infinite-horizon general-sum stochastic games

26-May Jason Marden UCSB

TBD

06-Jan Chara Podimata Harvard

Contextual Search in the Presence of Irrational Agents

13-Jan Jonathan Ullman Northeastern University

The Foundations of Private Statistical Estimation

20-Jan Ben Recht UC Berkeley

[POSTPONED to 21-April-2022] TBD

03-Feb Raphaël Berthier EPFL

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip

10-Feb Peyman Milanfar Google Research

Denoising as a Building Block for Imaging, Inverse Problems, and Machine Learning

24-Feb Julia Salzman Stanford

Statistical approaches for mechanistic biological discovery with genomics

03-Mar Daniel Russo Columbia Business School

Adaptivity and Confounding in Multi-Armed Bandit Experiments

10-Mar Ananda Theertha Suresh Google Research

Towards instance-optimal compression for distributed mean estimation

17-Mar Amin Karbasi Yale University

Palo Alto, We Have a Problem. There Is No Oracle!

23-Sep Joe Zhong Stanford

Interpolation Phase Transition in Neural Networks: Memorization and Generalization

30-Sep Amin Karbasi Yale

Sequential Decision Making: How Much Adaptivity Is Needed Anyways?

07-Oct Ashok Vardhan Makkuva UIUC

KO codes: Inventing Non-linear Encoding and Decoding for Reliable Wireless Communication via Deep-learning

14-Oct Hannah Li / Shi Dong Stanford

Experimentation and Decision-Making in Two-Sided Marketplaces: The Impact of Interference / Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States

21-Oct James Zou Stanford

AI for clinical trials and clinical trials for AI

28-Oct Murat Erdogdu University of Toronto

On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness

04-Nov Angelia Nedich Arizona State University

Distributed Algorithms for Optimization in Networks

11-Nov Baosen Zhang University of Washington

Safe and Efficient Reinforcement Learning for Power System Control

18-Nov Stephen Wright University of Wisconsin-Madison

Optimization in Theory and Practice

02-Dec Guy Bresler MIT

Connecting Statistical Problems with Different Structures

01-Apr Francis Bach INRIA

Finding Global Minima via Kernel Approximations

08-Apr Joel Tropp Caltech

Scalable semidefinite programming

15-Apr Yuxin Chen Princeton

Demystifying the Efficiency of Reinforcement Learning: Two Recent Stories

22-Apr Yihong Wu Yale

Recent results in planted assignment problems

29-Apr Kunal Talwar Apple

Private Stochastic Convex Optimization

06-May Aaron Sidford Stanford

Interior Point Methods for Nearly Linear Time Algorithms

13-May Csaba Szepesvari University of Alberta

[POSTPONED to 10-June-2021] Between tractable and intractable problems in reinforcement learning

20-May Inderjit S. Dhillon UT Austin

Multi-Scale Methods for Machine Learning

27-May Aarti Singh Carnegie Mellon University

Learning from preferences and labels

03-Jun Mert Pilanci Stanford

The Hidden Convex Optimization Landscape of Deep Neural Networks

10-Jun Csaba Szepesvari University of Alberta

Between tractable and intractable problems in reinforcement learning

14-Jan Nicholas Mastronarde & Jacob Chakareski State University of New York at Buffalo & New Jersey Institute of Technology

Accelerating Reinforcement Learning in Emerging Wireless IoT Systems and Applications via System Awareness

21-Jan Kevin Jamieson University of Washington

Adaptive Experimental Design for Best Identification and Multiple Testing

28-Jan Yoram Singer Princeton University

The Well Tempered Lasso

04-Feb Laura Waller UC Berkeley

End-to-end learning for computational microscopy

11-Feb Moritz Hardt UC Berkeley

Data, decisions, and dynamics

18-Feb Ramtin Pedarsani UC Santa Barbara

Enabling Fast and Robust Federated Learning

25-Feb Vitaly Feldman Apple AI Research

Chasing the Long Tail: What Neural Networks Memorize and Why

04-Mar Max Welling University of Amsterdam

Exploiting Symmetries in Inference and Learning

11-Mar Alon Orlitsky UC San Diego

Robust Learning from Batches -- The Best Things in Life are (Almost) Free

18-Mar Ashia Wilson MIT

Approximating cross-validation: guarantees for model assessment and selection

17-Sep Mark Wilde Louisiana State University

Quantum Renyi relative entropies and their use

01-Oct Carlee Joe-Wong CMU

Optimizing the Cost of Distributed Learning

08-Oct Alex Wein NYU's Courant Institute

Computational Barriers to Estimation from Low-Degree Polynomials

15-Oct Robert Nowak University of Wisconsin-Madison

Strategies for Active Machine Learning

22-Oct Adam Wierman Caltech

Competitive Control via Online Optimization

29-Oct Sujay Sanghavi UT Austin

Towards Model Agnostic Robustness

05-Nov Behnam Neyshabur Google

Learning Convolutions from Scratch

13-Nov David Woodruff CMU

A Very Sketchy Talk

19-Nov Ramesh Johari Stanford University

Interference in Experimental Design in Online Platforms

09-Apr Adam Wierman Caltech

[CANCELLED]

16-Apr Mark Wilde Louisiana State University

[CANCELLED]

23-Apr Baosen Zhang University of Washington

[CANCELLED]

30-Apr Carlee Joe Wong CMU

[CANCELLED]

07-May Pablo Parillo MIT

[CANCELLED]

14-May David Woodruff CMU

[CANCELLED]

21-May Jason Marden UC Santa Barbara

[CANCELLED]

28-May Moritz Hardt UC Berkeley

[CANCELLED]

04-Jun Behnam Neyshabur Google

[CANCELLED]

09-Jan Justin Gilmer Google Brain

The Robustness Problem

16-Jan Umesh Vazirani UC Berkeley

Theoretical Reflections on Quantum Supremacy

23-Jan Balaji Prabhakar Stanford

Self-Programming Networks: Applications to Financial Trading Systems

30-Jan C.-C. Jay Kuo USC

From Feedforward-Designed Convolutional Neural Networks (FF-CNNs) to Successive Subspace Learning (SSL)

06-Feb Sébastien Bubeck Microsoft Research Redmond

How to trap a gradient flow

13-Feb Scott Linderman Stanford

Recurrent Switching Linear Dynamical Systems for Neural and Behavioral Analysis

20-Feb Francesco Bullo UC Santa Barbara

Network Systems, Kuramoto Oscillators, and Synchronous Power Flow

27-Feb Christos Thrampoulidis UC Santa Barbara

Empirical Risk Minimization in High-dimensions: Asymptotics, Optimality and Double Descent

05-Mar Henry Arguello Fuentes Universidad Industrial de Santander

Codification Design in Compressive Imaging

12-Mar Daniel Russo Columbia

[CANCELLED] Global Optimality Guarantees for Policy Gradient Methods