Search Results for author: Zhixuan Fang

Found 8 papers, 1 papers with code

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

no code implementations13 Feb 2018 Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang

Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module.

reinforcement-learning Reinforcement Learning (RL)

Continuous Mean-Covariance Bandits

no code implementations NeurIPS 2021 Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang

To the best of our knowledge, this is the first work that considers option correlation in risk-aware bandits and explicitly quantifies how arbitrary covariance structures impact the learning performance.

Decision Making

Simultaneously Achieving Sublinear Regret and Constraint Violations for Online Convex Optimization with Time-varying Constraints

no code implementations15 Nov 2021 Qingsong Liu, Wenfei Wu, Longbo Huang, Zhixuan Fang

In this paper, we develop a novel virtual-queue-based online algorithm for online convex optimization (OCO) problems with long-term and time-varying constraints and conduct a performance analysis with respect to the dynamic regret and constraint violations.

Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning

1 code implementation30 Aug 2022 Pihe Hu, Ling Pan, Yu Chen, Zhixuan Fang, Longbo Huang

Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing.

Cloud Computing reinforcement-learning +2

The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation

no code implementations14 Dec 2023 Rongwu Xu, Brian S. Lin, Shujian Yang, Tianqi Zhang, Weiyan Shi, Tianwei Zhang, Zhixuan Fang, Wei Xu, Han Qiu

Therefore, in this study, we delve into LLMs' susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly.

Misinformation

Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training

no code implementations21 Jan 2024 Rongwu Xu, Zhixuan Fang

This paper presents Tempo, the first cloud-based deep learning system that cooperates with TEE and distributed GPUs for efficient DNN training with model confidentiality preserved.

RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning

no code implementations7 Mar 2024 Boning Li, Zhixuan Fang, Longbo Huang

Effective action abstraction is crucial in tackling challenges associated with large action spaces in Imperfect Information Extensive-Form Games (IIEFGs).

counterfactual Reinforcement Learning (RL)

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