Past Program Activities

Writing About Technical Topics for a General Audience
Speaker: Brian Christian
Dec. 7, 2020
Program: Theory of Reinforcement Learning, Probability, Geometry, and Computation in High Dimensions
Reading Group: Causality and Econometrics
Dec. 4, 2020
Program: Theory of Reinforcement Learning
Open Problem Discussion
Dec. 2, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Dec. 2, 2020
Program: Theory of Reinforcement Learning
Computational/Statistical Gaps for Learning Neural Networks
Speaker: Adam Klivans (University of Texas, Austin)
Dec. 1, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Reading Group: Causality and Econometrics
Nov. 27, 2020
Program: Theory of Reinforcement Learning
Open Problem Discussion
Nov. 25, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Nov. 25, 2020
Program: Theory of Reinforcement Learning
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Speaker: Simon S. Du (University of Washington)
Nov. 24, 2020
Program: Theory of Reinforcement Learning
Reading Group: Causality and Econometrics
Nov. 20, 2020
Program: Theory of Reinforcement Learning
Fellows Talk - Christina Yu
Speaker: Christina Yu (Cornell University)
Nov. 19, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Open Problem Discussion
Nov. 18, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Nov. 18, 2020
Program: Theory of Reinforcement Learning
Black-Box Control for Linear Dynamical Systems
Speaker: Xinyi Chen (Google / Princeton)
Nov. 17, 2020
Program: Theory of Reinforcement Learning
Reading Group: Causality and Econometrics
Nov. 13, 2020
Program: Theory of Reinforcement Learning
Fellows Talk - Vidya Muthukumar
Speaker: Vidya Muthukumar (UC Berkeley)
Nov. 12, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Open Problem Discussion
Nov. 11, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Nov. 11, 2020
Program: Theory of Reinforcement Learning
The Planted Matching Problem
Speaker: Cris Moore (Santa Fe Institute)
Nov. 10, 2020
Program: Probability, Geometry, and Computation in High Dimensions
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Speaker: Alekh Agarwal (Google / Princeton)
Nov. 10, 2020
Program: Theory of Reinforcement Learning
Reading Group: Causality and Econometrics
Nov. 6, 2020
Program: Theory of Reinforcement Learning
Open Problem Discussion
Nov. 4, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Nov. 4, 2020
Program: Theory of Reinforcement Learning
Work in Progress: a Structured Sampling Package
Speaker: Yin Tat Lee (University of Washington)
Nov. 3, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Corruption-Robust Exploration in Episodic Reinforcement Learning
Speaker: Thodoris Lykouris (Microsoft Research)
Nov. 3, 2020
Program: Theory of Reinforcement Learning
Reading Group: Causality and Econometrics
Oct. 30, 2020
Program: Theory of Reinforcement Learning
Fellows Talk: Vishesh Jain
Speaker: Vishesh Jain (UC Berkeley)
Oct. 29, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Open Problem Discussion
Oct. 28, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Oct. 28, 2020
Program: Theory of Reinforcement Learning
Thin Shell Estimates for Maximal Affine Surface Area
Speaker: Elisabeth Werner (Case Western Reserve University)
Oct. 27, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Reading Group: Causality and Econometrics
Oct. 23, 2020
Program: Theory of Reinforcement Learning
Fellows Talk - Jalaj Bhandari
Speaker: Jalaj Bhandari (Columbia University)
Oct. 22, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Open Problem Discussion
Oct. 21, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Oct. 21, 2020
Program: Theory of Reinforcement Learning
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Speaker: Marc Abeille (Criteo)
Oct. 20, 2020
Program: Theory of Reinforcement Learning
Reading Group: Causality and Econometrics
Oct. 16, 2020
Program: Theory of Reinforcement Learning
Fellows Talk - Tselil Schramm & Zhuoran Yang
Speaker: Tselil Schramm (Stanford University); Zhuoran Yang (Princeton University)
Oct. 15, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Open Problem Discussion
Oct. 14, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Oct. 14, 2020
Program: Theory of Reinforcement Learning
On (Non-Traditional) Costs and Potentials
Speaker: Shiri Artstein (Tel-Aviv University)
Oct. 13, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Reading Group: Causality and Econometrics
Oct. 9, 2020
Program: Theory of Reinforcement Learning
Open Problem Discussion
Oct. 7, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Oct. 7, 2020
Program: Theory of Reinforcement Learning
Robustly Learning Mixtures of (Clusterable) Gaussians via the SoS Proofs to Algorithms Method
Speaker: Sam Hopkins, UC Berkeley
Oct. 6, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning
Speaker: Yu-Xiang Wang (UC Santa Barbara)
Oct. 6, 2020
Program: Theory of Reinforcement Learning
On Distance Approximation for Graph Properties
Speaker: Dana Ron, Tel-Aviv University
Sep. 29, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Reading Group: Causality and Econometrics
Sep. 25, 2020
Program: Theory of Reinforcement Learning
Fellows Talk - Ashwin Pananjady
Speaker: Ashwin Pananjady, UC Berkeley
Sep. 24, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Open Problem Discussion
Sep. 23, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Sep. 23, 2020
Program: Theory of Reinforcement Learning
RL Seminar: Provably Efficient Exploration in Policy Optimization
Speaker: Zhuoran Yang (Princeton)
Sep. 22, 2020
Program: Theory of Reinforcement Learning
Open Problem Discussion
Sep. 18, 2020
Program: Theory of Reinforcement Learning
Reading Group: Causality and Econometrics
Sep. 18, 2020
Program: Theory of Reinforcement Learning
Reading Group: Deep RL and Function Approximation
Sep. 18, 2020
Program: Theory of Reinforcement Learning
Fellows Talk - Cindy Rush and Erik Waingarten
Speaker: Cindy Rush (Columbia University) & Erik Waingarten (Stanford University)
Sep. 17, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Nodal Domains of G(n,p) Graphs
Speaker: Mark Rudelson (University of Michigan)
Sep. 15, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Evaluating AI Decision Support Tools as Embedded Systems
Speaker: Sina Fazelpour (Carnegie Mellon University)
Sep. 10, 2020
Program: Summer Cluster: Interpretable Machine Learning
Fellows Talk - Galyna Livshyts and Lin Yang
Speaker: Galyna Livshyts (Georgia Institute of Technology) & Lin Yang (UCLA)
Sep. 10, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Polynomial Time Trace Reconstruction in the Smoothed Complexity Model
Speaker: Anindya De (University of Pennsylvania)
Sep. 8, 2020
Program: Probability, Geometry, and Computation in High Dimensions
The Interpolation Phase Transition in Neural Networks: Memorization and Generalization Under Lazy Training
Speaker: Andrea Montanari (Stanford University)
Sep. 3, 2020
Program: Probability, Geometry, and Computation in High Dimensions
Fellows Talk - The Kikuchi Hierarchy & Tensor PCA
Speaker: Ahmed El Alaoui, Stanford University
Aug. 27, 2020
Program: Probability, Geometry, and Computation in High Dimensions, Theory of Reinforcement Learning
Explainable k-Means and k-Medians Clustering
Speaker: Michal Moshkovitz
Aug. 10, 2020
Program: Summer Cluster: Interpretable Machine Learning
Why Causal Interpretations Matter for Algorithmic Bias Mitigation: A Legal Perspective
Speaker: Alice Xiang (Partnership on AI)
Aug. 6, 2020
Program: Summer Cluster: Interpretable Machine Learning
A Critical Look At Some Common Trends In IML Research
Speaker: Shai Ben-David (University of Waterloo)
Aug. 3, 2020
Program: Summer Cluster: Interpretable Machine Learning
Identifying Explanatory Training Examples via Relative Influence
Speaker: Karolina Dziugaite (Element AI)
Aug. 3, 2020
Program: Summer Cluster: Interpretable Machine Learning
Discovering Compositional Representations With Causal Graphs and Sketch Drawings
Speaker: Richard Zemel
Jul. 30, 2020
Program: Summer Cluster: Interpretable Machine Learning
Disentangled Interpretations and How We Can Use Them
Speaker: Chandan Singh (UC Berkeley)
Jul. 27, 2020
Program: Summer Cluster: Interpretable Machine Learning
Verifiably Truthful Mechanisms
Speaker: Simina Branzei (Purdue University)
Jul. 23, 2020
Program: Summer Cluster: Interpretable Machine Learning
A Unifying Philosophical Theory of AI Explanations
Speaker: Atoosa Kasirzadeh (University of Toronto & Australian National University)
Jul. 6, 2020
Program: Summer Cluster: Interpretable Machine Learning
Learning from Simple Explanations
Speaker: Sanjoy Dasgupta (UCSD)
Jul. 2, 2020
Program: Summer Cluster: Interpretable Machine Learning
Kickoff Meeting
Jun. 29, 2020
Program: Summer Cluster: Interpretable Machine Learning
Daily Tea
May 15, 2020
Indistinguishability Obfuscation from Simple-to-State Hard Problems: New Assumptions, New Techniques, and Simplification
Speaker: Aayush Jain (UCLA)
May 14, 2020
Program: Lattices: Algorithms, Complexity, and Cryptography
Daily Tea
May 14, 2020
Daily Tea
May 13, 2020
Daily Tea
May 12, 2020
Quantum Linear Algebra With Near-Optimal Complexities
Speaker: Lin Lin (UC Berkeley)
May 12, 2020
Program: The Quantum Wave in Computing
Daily Tea
May 11, 2020
Reading Group on MIP* = RE and its Application to Connes' Embedding Problem
May 8, 2020
Daily Tea
May 8, 2020
Daily Tea
May 7, 2020
Candidate iO from Homomorphic Encryption Schemes
Speaker: Sanjam Garg (UC Berkeley)
May 7, 2020
Program: Lattices: Algorithms, Complexity, and Cryptography
Daily Tea
May 6, 2020
Daily Tea
May 5, 2020
Sieving in practice: The Generalized Sieve Kernel (G6K)
Speaker: Elena Kirshanova (I.Kant Baltic Federal University)
May 5, 2020
Program: Lattices: Algorithms, Complexity, and Cryptography
Daily Tea
May 4, 2020
Reading Group on MIP* = RE and its Application to Connes' Embedding Problem
May 1, 2020
Daily Tea
May 1, 2020
Daily Tea
Apr. 30, 2020
Daily Tea
Apr. 29, 2020
Daily Tea
Apr. 28, 2020
A Theory of Trotter Error
Speaker: Yuan Su (UMD)
Apr. 28, 2020
Program: The Quantum Wave in Computing
Daily Tea
Apr. 27, 2020
Reading Group on MIP* = RE and its Application to Connes' Embedding Problem
Apr. 24, 2020
Daily Tea
Apr. 24, 2020
Daily Tea
Apr. 23, 2020
Watermarking and Traitor Tracing for PRFs
Speaker: David Wu (University of Virginia)
Apr. 23, 2020
Program: Lattices: Algorithms, Complexity, and Cryptography
Daily Tea
Apr. 22, 2020
Daily Tea
Apr. 21, 2020
Quantum Coupon Collector
Speaker: Ronald de Wolf (QuSoft, CWI and University of Amsterdam)
Apr. 21, 2020
Program: The Quantum Wave in Computing

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