Towards provably safe mixed transportation systems with human-driven and automated vehicles. - Xi Liu 011; Ke Ma 003; P. R. Kumar 001 (CDC, 2015)

Towards safety of transportation systems with a mixture of automated and human-driven vehicles. - Xi Liu 011; P. R. Kumar 001 (COMSNETS, 2016)

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)

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)

A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games. - Xi Liu 011; Muhe Xie; Xidao Wen; Rui Chen 012; Yong Ge; Nick Duffield; Na Wang (ICDM, 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)

Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction. - Arman Hasanzadeh; Xi Liu 011; Nick Duffield; Krishna R. Narayanan (IEEE BigData, 2019)

A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding. - Xianshan Qu; Li Li 035; Xi Liu 011; Rui Chen 012; Yong Ge; Soo-Hyun Choi (IEEE BigData, 2019)

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)

FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns. - Xidao Wen; Konstantinos Pelechrinis; Yu-Ru Lin; Xi Liu 011; Yongsu Ahn; Nan Cao 001 (IEEE VIS (Short Papers), 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)

Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses. - Xidao Wen; Yu-Ru Lin; Xi Liu 011; Peter Brusilovsky; Jordan Barria-Pineda (WWW, 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)

Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis. - Hanzi Mao; Xi Liu 011; Nick Duffield; Hao Yuan 001; Shuiwang Ji; Binayak P. Mohanty (ICDM, 2020)

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)

Micro- and macro-level churn analysis of large-scale mobile games. - Xi Liu 011; Muhe Xie; Xidao Wen; Rui Chen 012; Yong Ge; Nick Duffield; Na Wang (Knowl. Inf. Syst., 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)

Reward Biased Maximum Likelihood Estimation for Reinforcement Learning. - Akshay Mete; Rahul Singh 001; Xi Liu 011; P. R. Kumar 001 (L4DC, 2021)

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

Learning from Small Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales. - Tao Liu 035; P. R. Kumar 001; Xi Liu 011 (CoRR, 2021)

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales. - Tao Liu 035; P. R. Kumar 001; Ruida Zhou; Xi Liu 011 (NeurIPS, 2022)

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)

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)