Simon S Du
39 papers · 2016–2024 · 2 conferences · across top CS/AI conferences
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Conferences
NIPS (37)
COLT (2)
Top co-authors
Keywords
sample complexity
(14)
gradient descent
(6)
markov decision process
(6)
regret bound
(5)
reinforcement learning
(4)
offline reinforcement learning
(4)
representation learning
(3)
nash equilibrium
(3)
non-convex optimization
(3)
markov game
(3)
multi-agent system
(3)
robust optimization
(2)
linear function approximation
(2)
multi-task learning
(2)
optimal policy
(2)
active learning
(2)
global convergence
(2)
neural tangent kernel
(2)
matrix factorization
(2)
multi-agent reinforcement learning
(2)
Papers
Understanding the Gains from Repeated Self-Distillation
NIPS 2024
Settling the sample complexity of online reinforcement learning
COLT 2024
Optimal Multi-Distribution Learning
COLT 2024
Learning Optimal Tax Design in Nonatomic Congestion Games
NIPS 2024
Learning to Cooperate with Humans using Generative Agents
NIPS 2024
Decoding-Time Language Model Alignment with Multiple Objectives
NIPS 2024
Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models
NIPS 2024
Active representation learning for general task space with applications in robotics
NIPS 2023
Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer
NIPS 2023
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
NIPS 2023
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
NIPS 2023
Learning in Congestion Games with Bandit Feedback
NIPS 2022
When are Offline Two-Player Zero-Sum Markov Games Solvable?
NIPS 2022
On Gap-dependent Bounds for Offline Reinforcement Learning
NIPS 2022
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
NIPS 2022
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
NIPS 2022
Provable General Function Class Representation Learning in Multitask Bandits and MDP
NIPS 2022
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
NIPS 2021
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization
NIPS 2021
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
NIPS 2021
Nearly Horizon-Free Offline Reinforcement Learning
NIPS 2021
Corruption Robust Active Learning
NIPS 2021
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
NIPS 2020
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
NIPS 2020
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
NIPS 2020
On Reward-Free Reinforcement Learning with Linear Function Approximation
NIPS 2020
Planning with General Objective Functions: Going Beyond Total Rewards
NIPS 2020
Is Long Horizon RL More Difficult Than Short Horizon RL?
NIPS 2020
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
NIPS 2019
On Exact Computation with an Infinitely Wide Neural Net
NIPS 2019
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
NIPS 2019
Towards Understanding the Importance of Shortcut Connections in Residual Networks
NIPS 2019
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
NIPS 2019
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
NIPS 2018
How Many Samples are Needed to Estimate a Convolutional Neural Network?
NIPS 2018
Hypothesis Transfer Learning via Transformation Functions
NIPS 2017
Gradient Descent Can Take Exponential Time to Escape Saddle Points
NIPS 2017
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
NIPS 2017
Efficient Nonparametric Smoothness Estimation
NIPS 2016