Ruosong Wang
29 papers · 2017–2025 · 5 venues · across top CS/AI venues
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Research topics
Machine Learning
(27)
Optimization & Theory Β· Machine Learning
(22)
Reinforcement Learning
(13)
Methods
(13)
Learning Types Β· Machine Learning
(11)
Core Methods
(8)
Deep Learning
(4)
Mathematics & Optimization
(4)
Optimization
(4)
Artificial Intelligence
(3)
Architectures
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Core AI
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Learning Types Β· Deep Learning
(2)
Optimization & Theory Β· Deep Learning
(2)
Mathematics
(1)
Keywords
sample complexity
(10)
regret bound
(8)
reinforcement learning
(6)
kernel methods
(4)
neural tangent kernel
(3)
lower bound
(3)
markov decision process
(3)
distribution shift
(3)
representation learning
(3)
linear function approximation
(3)
function approximation
(3)
convolutional neural network
(2)
offline reinforcement learning
(2)
eluder dimension
(2)
optimal policy
(2)
multi-armed bandit
(2)
kernel regression
(2)
policy evaluation
(2)
value function approximation
(2)
generalization bound
(2)
Papers
Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback
ICML 2025
Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
ICLR 2025
Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds
ICLR 2025
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
NeurIPS 2024
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
ICML 2023
Provably Efficient Reinforcement Learning via Surprise Bound
AISTATS 2023
Variance-Aware Sparse Linear Bandits
ICLR 2023
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap
NeurIPS 2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL
ICML 2021
Instabilities of Offline RL with Pre-Trained Neural Representation
ICML 2021
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
ICLR 2021
What are the Statistical Limits of Offline RL with Linear Function Approximation?
ICLR 2021
Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
NeurIPS 2020
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
ICLR 2020
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
ICLR 2020
Is Long Horizon RL More Difficult Than Short Horizon RL?
NeurIPS 2020
Nearly Linear Row Sampling Algorithm for Quantile Regression
ICML 2020
On Reward-Free Reinforcement Learning with Linear Function Approximation
NeurIPS 2020
Planning with General Objective Functions: Going Beyond Total Rewards
NeurIPS 2020
Preference-based Reinforcement Learning with Finite-Time Guarantees
NeurIPS 2020
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
NeurIPS 2020
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
NeurIPS 2020
Dimensionality Reduction for Tukey Regression
ICML 2019
Efficient Symmetric Norm Regression via Linear Sketching
NeurIPS 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
ICML 2019
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
NeurIPS 2019
On Exact Computation with an Infinitely Wide Neural Net
NeurIPS 2019
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
NeurIPS 2019
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
COLT 2017