Tianhao Wang

According to our database, Tianhao Wang authored at least 17 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders.

Siyu Chen, Heejune Sheen, Xuyuan Xiong, Tianhao Wang, Zhuoran Yang

CoRR, June, 2025

Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model.

Siyu Chen, Beining Wu, Miao Lu, Zhuoran Yang, Tianhao Wang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Implicit Regularization of Gradient Flow on One-Layer Softmax Attention.

Heejune Sheen, Siyu Chen, Tianhao Wang, Harrison H. Zhou

CoRR, 2024

Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality.

Siyu Chen, Heejune Sheen, Tianhao Wang, Zhuoran Yang

CoRR, 2024

Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers.

Siyu Chen, Heejune Sheen, Tianhao Wang, Zhuoran Yang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality (extended abstract).

Siyu Chen, Heejune Sheen, Tianhao Wang, Zhuoran Yang

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning.

Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang

CoRR, 2023

Noise-Adaptive Thompson Sampling for Linear Contextual Bandits.

Ruitu Xu, Yifei Min, Tianhao Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation.

Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 2023

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning.

Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets.

Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits.

Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Stochastic Shortest Path with Linear Function Approximation.

Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 2022

2021

Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints.

Tianhao Wang, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Variance-Aware Off-Policy Evaluation with Linear Function Approximation.

Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2018

Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions.

Pan Xu, Tianhao Wang, Quanquan Gu

Proceedings of the 35th International Conference on Machine Learning, 2018

Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms.

Pan Xu, Tianhao Wang, Quanquan Gu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018