Regional Multi-Armed Bandits. - Zhiyang Wang; Ruida Zhou; Cong Shen 001 (AISTATS, 2018)

Generalized Global Bandit and Its Application in Cellular Coverage Optimization. - Cong Shen 001; Ruida Zhou; Cem Tekin; Mihaela van der Schaar (IEEE J. Sel. Top. Signal Process., 2018)

Regional Multi-Armed Bandits With Partial Informativeness. - Zhiyang Wang; Ruida Zhou; Cong Shen 001 (IEEE Trans. Signal Process., 2018)

Online Learning with Diverse User Preferences. - Chao Gan; Jing Yang 002; Ruida Zhou; Cong Shen 001 (ISIT, 2019)

A Cascading Bandit Approach To Efficient Mobility Management In Ultra-Dense Networks. - Chao Wang 073; Ruida Zhou; Jing Yang 002; Cong Shen 001 (MLSP, 2019)

Cost-Aware Learning and Optimization for Opportunistic Spectrum Access. - Chao Gan; Ruida Zhou; Jing Yang 002; Cong Shen 001 (IEEE Trans. Cogn. Commun. Netw., 2019)

Weakly Private Information Retrieval Under the Maximal Leakage Metric. - Ruida Zhou; Tao Guo 003; Chao Tian 002 (ISIT, 2020)

Neighbor Cell List Optimization in Handover Management Using Cascading Bandits Algorithm. - Chao Wang 073; Jian Yang 014; Huasen He; Ruida Zhou; Shuangwu Chen; Xiaofeng Jiang (IEEE Access, 2020)

On the Information Leakage in Private Information Retrieval Systems. - Tao Guo 003; Ruida Zhou; Chao Tian 002 (IEEE Trans. Inf. Forensics Secur., 2020)

Capacity-Achieving Private Information Retrieval Codes From MDS-Coded Databases With Minimum Message Size. - Ruida Zhou; Chao Tian 002; Hua Sun 001; Tie Liu 002 (IEEE Trans. Inf. Theory, 2020)

Cost-Aware Cascading Bandits. - Chao Gan; Ruida Zhou; Jing Yang 002; Cong Shen 001 (IEEE Trans. Signal Process., 2020)

Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs. - Tao Liu 035; Ruida Zhou; Dileep Kalathil; Panganamala R. Kumar; Chao Tian 002 (NeurIPS, 2021)

Fast Global Convergence of Policy Optimization for Constrained MDPs. - Tao Liu 035; Ruida Zhou; Dileep Kalathil; P. R. Kumar 001; Chao Tian 002 (CoRR, 2021)

Computational Techniques for Investigating Information Theoretic Limits of Information Systems. - Chao Tian 002; James S. Plank; Brent Hurst; Ruida Zhou (Inf., 2021)

New Results on the Storage-Retrieval Tradeoff in Private Information Retrieval Systems. - Tao Guo 003; Ruida Zhou; Chao Tian 002 (IEEE J. Sel. Areas Inf. Theory, 2021)

Approximate Top-m Arm Identification with Heterogeneous Reward Variances. - Ruida Zhou; Chao Tian 002 (AISTATS, 2022)

Improved Weakly Private Information Retrieval Codes. - Chengyuan Qian; Ruida Zhou; Chao Tian 002; Tie Liu 002 (ISIT, 2022)

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)

Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning. - Ruida Zhou; Tao Liu 035; Dileep Kalathil; P. R. Kumar 001; Chao Tian 002 (NeurIPS, 2022)

Two-Level Private Information Retrieval. - Ruida Zhou; Chao Tian 002; Hua Sun 001; James S. Plank (IEEE J. Sel. Areas Inf. Theory, 2022)

Individually Conditional Individual Mutual Information Bound on Generalization Error. - Ruida Zhou; Chao Tian 002; Tie Liu 002 (IEEE Trans. Inf. Theory, 2022)

On Top-k Selection From m-Wise Partial Rankings via Borda Counting. - Wenjing Chen 001; Ruida Zhou; Chao Tian 002; Cong Shen 001 (IEEE Trans. Signal Process., 2022)

Federated Linear Bandits with Finite Adversarial Actions. - Li Fan; Ruida Zhou; Chao Tian 002; Cong Shen 001 (NeurIPS, 2023)

Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games. - Youbang Sun; Tao Liu 035; Ruida Zhou; P. R. Kumar 001; Shahin Shahrampour (NeurIPS, 2023)

Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation. - Ruida Zhou; Tao Liu 035; Min Cheng; Dileep Kalathil; P. R. Kumar 001; Chao Tian 002 (NeurIPS, 2023)

Stochastic chaining and strengthened information-theoretic generalization bounds. - Ruida Zhou; Chao Tian 002; Tie Liu 002 (J. Frankl. Inst., 2023)

Provable Policy Gradient Methods for Average-Reward Markov Potential Games. - Min Cheng; Ruida Zhou; P. R. Kumar 001; Chao Tian 002 (AISTATS, 2024)

Latent 3D Graph Diffusion. - Yuning You; Ruida Zhou; Jiwoong Park; Haotian Xu 004; Chao Tian 002; Zhangyang Wang; Yang Shen 001 (ICLR, 2024)

Path-Guided Particle-based Sampling. - Mingzhou Fan; Ruida Zhou; Chao Tian 002; Xiaoning Qian (ICML, 2024)

When Uncertainty-Based Active Learning May Fail? - Amir Hossein Rahmati; Mingzhou Fan; Ruida Zhou; Nathan M. Urban; Byung-Jun Yoon; Xiaoning Qian (ICPR (1), 2024)

Research on Characteristics of Hesitant Driving Behavior in Urban Expressway Diversion Areas. - Xiang Li; Ruida Zhou; Lanfang Zhang; Jun Zou; Jun Li (IoTAAI, 2024)

Staggered Quantizers for Perfect Perceptual Quality: A Connection Between Quantizers with Common Randomness and Without. - Ruida Zhou; Chao Tian 002 (ISIT (Workshops), 2024)

Personalized Heterogeneous Gaussian Mean Estimation Under Communication Constraints. - Ruida Zhou; Suhas N. Diggavi (ISIT, 2024)

From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization. - Qiang Zhang; Ruida Zhou; Yang Shen; Tie Liu 002 (CoRR, 2024)

Correlational Lagrangian Schrödinger Bridge: Learning Dynamics with Population-Level Regularization. - Yuning You; Ruida Zhou; Yang Shen 001 (CoRR, 2024)

Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. - Kaan Ozkara; Bruce Huang; Ruida Zhou; Suhas N. Diggavi (CoRR, 2024)

Understanding Uncertainty-based Active Learning Under Model Mismatch. - Amir Hossein Rahmati; Mingzhou Fan; Ruida Zhou; Nathan M. Urban; Byung-Jun Yoon; Xiaoning Qian (CoRR, 2024)

Reframing Data Value for Large Language Models Through the Lens of Plausability. - Mohamad Rida Rammal; Ruida Zhou; Suhas N. Diggavi (CoRR, 2024)

Transformers learn variable-order Markov chains in-context. - Ruida Zhou; Chao Tian 002; Suhas N. Diggavi (CoRR, 2024)

On the Training Convergence of Transformers for In-Context Classification. - Wei Shen; Ruida Zhou; Jing Yang 002; Cong Shen 001 (CoRR, 2024)

Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem. - Ruida Zhou; Chao Tian 002; Tie Liu 002 (IEEE J. Sel. Areas Inf. Theory, 2024)

Harnessing the Power of Federated Learning in Federated Contextual Bandits. - Chengshuai Shi; Ruida Zhou; Kun Yang 011; Cong Shen 001 (Trans. Mach. Learn. Res., 2024)

ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. - Kaan Ozkara; Bruce Huang; Ruida Zhou; Suhas N. Diggavi (AISTATS, 2025)

Cost-Aware Optimal Pairwise Pure Exploration. - Di Wu; Chengshuai Shi; Ruida Zhou; Cong Shen 001 (AISTATS, 2025)

Sparse MoE as a New Treatment: Addressing Forgetting, Fitting, Learning Issues in Multi-Modal Multi-Task Learning. - Jie Peng 002; Sukwon Yun; Kaixiong Zhou; Ruida Zhou; Thomas Hartvigsen; Yanyong Zhang; Zhangyang Wang; Tianlong Chen 001 (CPAL, 2025)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning. - Fengyu Gao; Ruida Zhou; Tianhao Wang 001; Cong Shen 001; Jing Yang 002 (ICLR, 2025)

On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery. - Renpu Liu; Ruida Zhou; Cong Shen 001; Jing Yang 002 (ICLR, 2025)

SPIRE: Conditional Personalization for Federated Diffusion Generative Models. - Kaan Ozkara; Ruida Zhou; Suhas N. Diggavi (CoRR, 2025)

On the optimal regret of collaborative personalized linear bandits. - Bruce Huang; Ruida Zhou; Lin F. Yang; Suhas N. Diggavi (CoRR, 2025)

Weakly Private Information Retrieval From Heterogeneously Trusted Servers. - Wenyuan Zhao; Yu-Shin Huang; Ruida Zhou; Chao Tian 002 (IEEE Trans. Inf. Theory, 2025)