168 citations · 361 across the 47 of their papers we have counts for
12 papers · 1 filter
Reconstructing Training Data from Model Gradient, Provably
Zihan Wang, Jason D. Lee, Qi Lei
Understanding when and how much a model gradient leaks information about the training sample is an important question in privacy. In this paper, we present a surprising result: eve…
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Satyen Kale, Jason D. Lee, Chris De Sa +2
Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has b…
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability
Alex Damian, Eshaan Nichani, Jason D. Lee
Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness , is bounded by , training is "stable" and the…
PAC Reinforcement Learning for Predictive State Representations
Wenhao Zhan, Masatoshi Uehara, Wen Sun +1
In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expr…
Neural Networks can Learn Representations with Gradient Descent
Alex Damian, Jason D. Lee, Mahdi Soltanolkotabi
Significant theoretical work has established that in specific regimes, neural networks trained by gradient descent behave like kernel methods. However, in practice, it is known tha…
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee +2
We study reinforcement learning with function approximation for large-scale Partially Observable Markov Decision Processes (POMDPs) where the state space and observation space are…
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee +2
We study Reinforcement Learning for partially observable dynamical systems using function approximation. We propose a new \textit{Partially Observable Bilinear Actor-Critic framewo…
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games
Wenhao Zhan, Jason D. Lee, Zhuoran Yang
We study decentralized policy learning in Markov games where we control a single agent to play with nonstationary and possibly adversarial opponents. Our goal is to develop a no-re…
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
Eshaan Nichani, Yu Bai, Jason D. Lee
A recent goal in the theory of deep learning is to identify how neural networks can escape the "lazy training," or Neural Tangent Kernel (NTK) regime, where the network is coupled…
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
Itay Safran, Gal Vardi, Jason D. Lee
We study the dynamics and implicit bias of gradient flow (GF) on univariate ReLU neural networks with a single hidden layer in a binary classification setting. We show that when th…
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
Jiaqi Yang, Qi Lei, Jason D. Lee +1
We give novel algorithms for multi-task and lifelong linear bandits with shared representation. Specifically, we consider the setting where we play linear bandits with dimensio…
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Wenhao Zhan, Baihe Huang, Audrey Huang +2
Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong assumptions on both the function classes (e.g., Bellman-completeness) and the data coverag…