QoE-Optimal Scheduling for On-Demand Video Streams over Unreliable Wireless Networks. - I-Hong Hou; Ping-Chun Hsieh (MobiHoc, 2015)

WiMAC: Rapid Implementation Platform for User Definable MAC Protocols Through Separation. - Simon Yau; Liang Ge; Ping-Chun Hsieh; I-Hong Hou; Shuguang Cui; P. R. Kumar 001; Amal Ekbal; Nikhil Kundargi (SIGCOMM, 2015)

VisualLink: Strengthening the Connection between Hearing-impaired Elderly and their Family. - Hsiang-Ting Hong; Tzu-Yu Su; Po-Hsun Lee; Ping-Chun Hsieh; Mian-Jhong Chiu (CHI Extended Abstracts, 2017)

Throughput-Optimal Scheduling for Multi-Hop Networked Transportation Systems With Switch-Over Delay. - Ping-Chun Hsieh; Xi Liu 011; Jian Jiao 006; I-Hong Hou; Yunlong Zhang; P. R. Kumar 001 (MobiHoc, 2017)

Delay-Optimal Scheduling for Queueing Systems with Switching Overhead. - Ping-Chun Hsieh; I-Hong Hou; Xi Liu 011 (CoRR, 2017)

The capacity of QoE for wireless networks with unreliable transmissions. - I-Hong Hou; Ping-Chun Hsieh (Queueing Syst. Theory Appl., 2017)

Safe Intersection Management for Mixed Transportation Systems with Human-Driven and Autonomous Vehicles. - Xi Liu 011; Ping-Chun Hsieh; P. R. Kumar 001 (Allerton, 2018)

An Experimental Study on Coverage Enhancement of LTE Cat-M1 for Machine-Type Communication. - Ping-Chun Hsieh; Yupeng Jia; Darwin Parra; Prabha Aithal (ICC, 2018)

A Decentralized Medium Access Protocol for Real-Time Wireless Ad Hoc Networks With Unreliable Transmissions. - Ping-Chun Hsieh; I-Hong Hou (ICDCS, 2018)

PULS: Processor-Supported Ultra-Low Latency Scheduling. - Simon Yau; Ping-Chun Hsieh; Rajarshi Bhattacharyya; K. R. Kartic Bhargav; Srinivas Shakkottai; I-Hong Hou; P. R. Kumar 001 (MobiHoc, 2018)

Heteroscedastic Bandits with Reneging. - Ping-Chun Hsieh; Xi Liu 011; Anirban Bhattacharya; P. R. Kumar 001 (CoRR, 2018)

Streaming Network Embedding through Local Actions. - Xi Liu 011; Ping-Chun Hsieh; Nick Duffield; Rui Chen 012; Muhe Xie; Xidao Wen (CoRR, 2018)

Heavy-Traffic Analysis of QoE Optimality for On-Demand Video Streams Over Fading Channels. - Ping-Chun Hsieh; I-Hong Hou (IEEE/ACM Trans. Netw., 2018)

Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging. - Ping-Chun Hsieh; Xi Liu 011; Anirban Bhattacharya; P. R. Kumar 001 (ICML, 2019)

Real-Time Streaming Graph Embedding Through Local Actions. - Xi Liu 011; Ping-Chun Hsieh; Nick Duffield; Rui Chen 012; Muhe Xie; Xidao Wen (WWW (Companion Volume), 2019)

Bandit Learning Through Biased Maximum Likelihood Estimation. - Xi Liu 011; Ping-Chun Hsieh; Anirban Bhattacharya; P. R. Kumar 001 (CoRR, 2019)

Fresher Content or Smoother Playback? A Brownian-Approximation Framework for Real-Time Video Delivery in Wireless Networks. - Ping-Chun Hsieh; Xi Liu 011; I-Hong Hou (CoRR, 2019)

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits. - Xi Liu 011; Ping-Chun Hsieh; Yu-Heng Hung; Anirban Bhattacharya; P. R. Kumar 001 (ICML, 2020)

Fresher content or smoother playback?: a brownian-approximation framework for scheduling real-time wireless video streams. - Ping-Chun Hsieh; Xi Liu 011; I-Hong Hou (MobiHoc, 2020)

Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning. - Xi Liu 011; Li Li 035; Ping-Chun Hsieh; Muhe Xie; Yong Ge; Rui Chen 012 (CoRR, 2020)

Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits. - Yu-Heng Hung; Ping-Chun Hsieh; Xi Liu 011; P. R. Kumar 001 (AAAI, 2021)

Optimal Wireless Scheduling for Remote Sensing through Brownian Approximation. - Daojing Guo; Ping-Chun Hsieh; I-Hong Hou (INFOCOM, 2021)

Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization. - Bing-Jing Hsieh; Ping-Chun Hsieh; Xi Liu 011 (NeurIPS, 2021)

NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL. - Khaled Nakhleh; Santosh Ganji; Ping-Chun Hsieh; I-Hong Hou; Srinivas Shakkottai (NeurIPS, 2021)

Escaping from zero gradient: Revisiting action-constrained reinforcement learning via Frank-Wolfe policy optimization. - Jyun-Li Lin; Wei Hung; Shang-Hsuan Yang; Ping-Chun Hsieh; Xi Liu 011 (UAI, 2021)

Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO. - Hsuan-Yu Yao; Ping-Chun Hsieh; Kuo-Hao Ho; Kai-Chun Hu; Liang-Chun Ouyang; I-Chen Wu (CoRR, 2021)

Deterministic Bandwidth-Based Packet-Level Traffic Splitting for Datacenter Networks. - Cheng-Yu Wu; Li-Hsing Yen; Ping-Chun Hsieh; Chien-Chao Tseng (APNOMS, 2022)

Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265. - Yung-Han Ho; Chia-Hao Kao; Wen-Hsiao Peng; Ping-Chun Hsieh (VCIP, 2022)

Neural Contextual Bandits via Reward-Biased Maximum Likelihood Estimation. - Yu-Heng Hung; Ping-Chun Hsieh (CoRR, 2022)

Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits: A Distributional Learning Perspective. - Yu-Heng Hung; Ping-Chun Hsieh (AAAI, 2023)

Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games. - Kuo-Hao Ho; Ping-Chun Hsieh; Chiu-Chou Lin; You-Ren Luo; Feng-Jian Wang; I-Chen Wu (ACML, 2023)

Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees. - Hsin-En Su; Yen-Ju Chen; Ping-Chun Hsieh; Xi Liu 011 (AISTATS, 2023)

Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots. - Wei Hung; Bo-Kai Huang; Ping-Chun Hsieh; Xi Liu 011 (ICLR, 2023)

Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization. - Yun-Hsuan Lien; Ping-Chun Hsieh; Yu-Shuen Wang (ICML, 2023)

Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs. - Yu-Heng Hung; Ping-Chun Hsieh; Akshay Mete; P. R. Kumar 001 (CoRR, 2023)

Accelerated Policy Gradient: On the Nesterov Momentum for Reinforcement Learning. - Yen-Ju Chen; Nai-Chieh Huang; Ping-Chun Hsieh (CoRR, 2023)

PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping. - Nai-Chieh Huang; Ping-Chun Hsieh; Kuo-Hao Ho; I-Chen Wu (AAAI, 2024)

Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning. - Yen-Ju Chen; Nai-Chieh Huang; Ching-pei Lee; Ping-Chun Hsieh (ICML, 2024)

Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning. - Yun-Hsuan Lien; Ping-Chun Hsieh; Tzu-Mao Li; Yu-Shuen Wang (ICML, 2024)

Diffusion-Reward Adversarial Imitation Learning. - Chun-Mao Lai; Hsiang-Chun Wang; Ping-Chun Hsieh; Yu-Chiang Frank Wang; Min-Hung Chen; Shao-Hua Sun (NeurIPS, 2024)

Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion. - Kuang-Da Wang; Wei-Yao Wang; Ping-Chun Hsieh; Wen-Chih Peng (ECML/PKDD (10), 2024)

Image Deraining via Self-supervised Reinforcement Learning. - He-Hao Liao; Yan-Tsung Peng; Wen-Tao Chu; Ping-Chun Hsieh; Chung-Chi Tsai (CoRR, 2024)

Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits. - Kuan-Ta Li; Ping-Chun Hsieh; Yu-Chih Huang (CoRR, 2024)

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL. - Yu-Heng Hung; Kai-Jie Lin; Yu-Heng Lin; Chien-Yi Wang; Cheng Sun; Ping-Chun Hsieh (ICLR, 2025)

Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs. - Wei Hung; Shao-Hua Sun; Ping-Chun Hsieh (ICLR, 2025)

DDOT: A Derivative-Directed Dual-Decoder Ordinary Differential Equation Transformer for Dynamic System Modeling. - Yang Chang; Kuang-Da Wang; Ping-Chun Hsieh; Cheng-Kuan Lin; Wen-Chih Peng (PAKDD (3), 2025)

Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective. - Yu-Wei Yang; Yun-Ming Chan; Wei Hung; Xi Liu 011; Ping-Chun Hsieh (CoRR, 2025)

Plan2Align: Predictive Planning Based Test-Time Preference Alignment in Paragraph-Level Machine Translation. - Kuang-Da Wang; Teng-Ruei Chen; Yu-Heng Hung; Shuoyang Ding; Yueh-Hua Wu; Yu-Chiang Frank Wang; Chao-Han Huck Yang; Wen-Chih Peng; Ping-Chun Hsieh (CoRR, 2025)