Ruosong Wang

Orcid: 0000-0002-2148-0993

According to our database1, Ruosong Wang authored at least 50 papers between 2016 and 2023.

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Bibliography

2023
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes.
Proceedings of the International Conference on Machine Learning, 2023

Variance-Aware Sparse Linear Bandits.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Provably Efficient Reinforcement Learning via Surprise Bound.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Tight Bounds for ℓ<sub>1</sub> Oblivious Subspace Embeddings.
ACM Trans. Algorithms, 2022

Horizon-Free Reinforcement Learning for Latent Markov Decision Processes.
CoRR, 2022

2021
Tight Bounds for the Subspace Sketch Problem with Applications.
SIAM J. Comput., 2021

Online Sub-Sampling for Reinforcement Learning with General Function Approximation.
CoRR, 2021

An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap.
CoRR, 2021

An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Instabilities of Offline RL with Pre-Trained Neural Representation.
Proceedings of the 38th International Conference on Machine Learning, 2021

Bilinear Classes: A Structural Framework for Provable Generalization in RL.
Proceedings of the 38th International Conference on Machine Learning, 2021

What are the Statistical Limits of Offline RL with Linear Function Approximation?
Proceedings of the 9th International Conference on Learning Representations, 2021

Optimism in Reinforcement Learning with Generalized Linear Function Approximation.
Proceedings of the 9th International Conference on Learning Representations, 2021

Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

2020
Planning with Submodular Objective Functions.
CoRR, 2020

Provably Efficient Reinforcement Learning with General Value Function Approximation.
CoRR, 2020

Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
CoRR, 2020

Provably Efficient Exploration for RL with Unsupervised Learning.
CoRR, 2020

Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity.
CoRR, 2020

The Communication Complexity of Optimization.
Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Preference-based Reinforcement Learning with Finite-Time Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Planning with General Objective Functions: Going Beyond Total Rewards.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Reward-Free Reinforcement Learning with Linear Function Approximation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Is Long Horizon RL More Difficult Than Short Horizon RL?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Nearly Linear Row Sampling Algorithm for Quantile Regression.
Proceedings of the 37th International Conference on Machine Learning, 2020

Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Proceedings of the 8th International Conference on Learning Representations, 2020

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Exponential Separations in the Energy Complexity of Leader Election.
ACM Trans. Algorithms, 2019

Enhanced Convolutional Neural Tangent Kernels.
CoRR, 2019

Continuous Control with Contexts, Provably.
CoRR, 2019

Tight Bounds for ℓp Oblivious Subspace Embeddings.
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

Efficient Symmetric Norm Regression via Linear Sketching.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On Exact Computation with an Infinitely Wide Neural Net.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Classical Algorithms from Quantum and Arthur-Merlin Communication Protocols.
Proceedings of the 10th Innovations in Theoretical Computer Science Conference, 2019

Dimensionality Reduction for Tukey Regression.
Proceedings of the 36th International Conference on Machine Learning, 2019

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Tight Bounds for 𝓁<sub>p</sub> Oblivious Subspace Embeddings.
CoRR, 2018

An Improved Algorithm for Incremental DFS Tree in Undirected Graphs.
Proceedings of the 16th Scandinavian Symposium and Workshops on Algorithm Theory, 2018

2017
k-Regret Minimizing Set: Efficient Algorithms and Hardness.
Proceedings of the 20th International Conference on Database Theory, 2017

Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration.
Proceedings of the 30th Conference on Learning Theory, 2017

Efficient Near-optimal Algorithms for Barter Exchange.
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

K-Memory Strategies in Repeated Games.
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

Bounded Rationality of Restricted Turing Machines.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Improved Algorithms for Maintaining DFS Tree in Undirected Graphs.
CoRR, 2016

How to Elect a Low-energy Leader.
CoRR, 2016


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