Simon Du
33 papers · 2015–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π§ Keyword Pioneer π Conference Polyglot (4)
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Hot Topic Early Bird
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Interdisciplinary Bridge
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Conference Polyglot
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Deep Specialist
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Keyword Collector
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Century Club
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The Questioner
Conferences
ICML (19)
AISTATS (6)
COLT (6)
ACL (2)
Top co-authors
Keywords
reinforcement learning
(10)
regret bound
(8)
sample complexity
(7)
gradient descent
(5)
representation learning
(4)
linear mdp
(3)
markov decision process
(3)
markov game
(2)
policy optimization
(2)
reward-free exploration
(2)
generalization bound
(2)
active learning
(2)
transfer learning
(2)
tabular reinforcement learning
(2)
linear convergence
(2)
episodic learning
(2)
function approximation
(2)
global convergence
(2)
adversarial corruption
(2)
global minimum
(2)
Papers
Anytime Acceleration of Gradient Descent
COLT 2025
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
ACL 2024
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
ACL 2024
Blessing of Class Diversity in Pre-training
AISTATS 2023
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron
COLT 2023
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
COLT 2023
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
ICML 2022
Gap-Dependent Bounds for Two-Player Markov Games
AISTATS 2022
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games
AISTATS 2022
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies
COLT 2022
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path
ICML 2022
Active Multi-Task Representation Learning
ICML 2022
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
ICML 2022
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
ICML 2022
Denoised MDPs: Learning World Models Better Than the World Itself
ICML 2022
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
COLT 2021
Q-learning with Logarithmic Regret
AISTATS 2021
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap
COLT 2021
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
ICML 2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL
ICML 2021
On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
ICML 2021
Near Optimal Reward-Free Reinforcement Learning
ICML 2021
Provable Representation Learning for Imitation Learning via Bi-level Optimization
ICML 2020
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
ICML 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks
ICML 2019
Provably efficient RL with Rich Observations via Latent State Decoding
ICML 2019
Gradient Descent Finds Global Minima of Deep Neural Networks
ICML 2019
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
ICML 2018
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
ICML 2018
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
ICML 2018
Gradient Descent Learns One-hidden-layer CNN: Donβt be Afraid of Spurious Local Minima
ICML 2018
Stochastic Zeroth-order Optimization in High Dimensions
AISTATS 2018
Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the NystrΓΆm Method
AISTATS 2015