Jiaqi Yang

According to our database, Jiaqi Yang authored at least 10 papers between 2020 and 2022.

Collaborative distances:

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

2022

Nearly Minimax Algorithms for Linear Bandits with Shared Representation.

Jiaqi Yang, Qi Lei, Jason D. Lee, Simon S. Du

CoRR, 2022

2021

Variance-Aware Confidence Set: Variance-Dependent Bound for Linear Bandits and Horizon-Free Bound for Linear Mixture MDP.

Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du

CoRR, 2021

Linear bandits with limited adaptivity and learning distributional optimal design.

Yufei Ruan, Jiaqi Yang, Yuan Zhou

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP.

Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du

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

Optimal Gradient-based Algorithms for Non-concave Bandit Optimization.

Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang

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

Going Beyond Linear RL: Sample Efficient Neural Function Approximation.

Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang

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

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature.

Kefan Dong, Jiaqi Yang, Tengyu Ma

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

Impact of Representation Learning in Linear Bandits.

Jiaqi Yang, Wei Hu, Jason D. Lee, Simon Shaolei Du

Proceedings of the 9th International Conference on Learning Representations, 2021

Fully Gap-Dependent Bounds for Multinomial Logit Bandit.

Jiaqi Yang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Provable Benefits of Representation Learning in Linear Bandits.

Jiaqi Yang, Wei Hu, Jason D. Lee, Simon S. Du

CoRR, 2020