Search Results for author: Jiheng Zhang

Found 10 papers, 4 papers with code

RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model

no code implementations18 Mar 2024 Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang

Up to a logarithmic dependence on the size of the state space, Lin-Confident-FTRL learns $\epsilon$-CCE with a provable optimal accuracy bound $O(\epsilon^{-2})$ and gets rids of the linear dependency on the action space, while scaling polynomially with relevant problem parameters (such as the number of agents and time horizon).

Stochastic Graph Bandit Learning with Side-Observations

no code implementations29 Aug 2023 Xueping Gong, Jiheng Zhang

In this paper, we investigate the stochastic contextual bandit with general function space and graph feedback.

Computational Efficiency Multi-Armed Bandits

Provably Efficient Learning in Partially Observable Contextual Bandit

no code implementations7 Aug 2023 Xueping Gong, Jiheng Zhang

We then show how causal bounds can be applied to improving classical bandit algorithms and affect the regrets with respect to the size of action sets and function spaces.

Multi-Armed Bandits Transfer Learning

Debiasing Recommendation by Learning Identifiable Latent Confounders

1 code implementation10 Feb 2023 Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, Ruocheng Guo

Confounding bias arises due to the presence of unmeasured variables (e. g., the socio-economic status of a user) that can affect both a user's exposure and feedback.

Causal Inference counterfactual +1

Single-Trajectory Distributionally Robust Reinforcement Learning

no code implementations27 Jan 2023 Zhipeng Liang, Xiaoteng Ma, Jose Blanchet, Jiheng Zhang, Zhengyuan Zhou

As a framework for sequential decision-making, Reinforcement Learning (RL) has been regarded as an essential component leading to Artificial General Intelligence (AGI).

Decision Making Q-Learning +2

Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles

1 code implementation21 Oct 2022 Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang, Jiheng Zhang

We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource consumption in a vector form.

Multi-Armed Bandits regression

Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation

no code implementations14 Sep 2022 Xiaoteng Ma, Zhipeng Liang, Jose Blanchet, Mingwen Liu, Li Xia, Jiheng Zhang, Qianchuan Zhao, Zhengyuan Zhou

Among the reasons hindering reinforcement learning (RL) applications to real-world problems, two factors are critical: limited data and the mismatch between the testing environment (real environment in which the policy is deployed) and the training environment (e. g., a simulator).

Offline RL reinforcement-learning +1

Dual Instrumental Method for Confounded Kernelized Bandits

no code implementations7 Sep 2022 Xueping Gong, Jiheng Zhang

The contextual bandit problem is a theoretically justified framework with wide applications in various fields.

On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits

1 code implementation16 Jun 2022 Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan YAO, Jiheng Zhang

To address such a challenge as the online convex optimization with privacy protection, we propose a private variant of online Frank-Wolfe algorithm with recursive gradients for variance reduction to update and reveal the parameters upon each data.

Multi-Armed Bandits

Generalized Linear Bandits with Local Differential Privacy

1 code implementation NeurIPS 2021 Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang

In this paper, we design LDP algorithms for stochastic generalized linear bandits to achieve the same regret bound as in non-privacy settings.

Decision Making Multi-Armed Bandits

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