← Authors

Ruosong Wang

29 papers · 2017–2025 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (32) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (13) 🌍 Conference Polyglot (5) 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (10) ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (89) πŸ“ˆ Trend Setter ❓ The Questioner (3) πŸ’Ž Century Club (29)

Conferences

NIPS (13) ICLR (7) ICML (7) AISTATS (1) COLT (1)

Top co-authors

Lin Yang (10) Simon S Du (8) Ruslan Salakhutdinov (7) Sham Kakade (4) Simon Shaolei Du (4) Zhiyuan Li (3) Jason Lee (3) Sanjeev Arora (3) Simon S. Du (2) Hanrui Zhang (2)

Keywords

sample complexity (8) reinforcement learning (5) regret bound (4) kernel methods (3) function approximation (3) linear function approximation (2) deterministic system (2) markov decision process (2) neural tangent kernel (2) generalization bound (2) optimal policy (2) distribution shift (2) eluder dimension (2) infinite width (2) representation learning (2) value function approximation (2) graph classification (1) sample efficiency (1) reinforcement learning theory (1) matrix factorization (1)

Papers

Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error ICLR 2025
Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback ICML 2025
Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds ICLR 2025
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning NIPS 2024
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes ICML 2023
Variance-Aware Sparse Linear Bandits ICLR 2023
Provably Efficient Reinforcement Learning via Surprise Bound AISTATS 2023
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap NIPS 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 NIPS 2020
Preference-based Reinforcement Learning with Finite-Time Guarantees NIPS 2020
On Reward-Free Reinforcement Learning with Linear Function Approximation NIPS 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
Nearly Linear Row Sampling Algorithm for Quantile Regression ICML 2020
Is Long Horizon RL More Difficult Than Short Horizon RL? NIPS 2020
Planning with General Objective Functions: Going Beyond Total Rewards NIPS 2020
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning NIPS 2020
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension NIPS 2020
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle NIPS 2019
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels NIPS 2019
Efficient Symmetric Norm Regression via Linear Sketching NIPS 2019
On Exact Computation with an Infinitely Wide Neural Net NIPS 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks ICML 2019
Dimensionality Reduction for Tukey Regression ICML 2019
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration COLT 2017