Zhou, Ruida

24 publications

ICLR 2026 Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback Amirhossein Afsharrad, Ruida Zhou, Luca Viano, Sanjay Lall, Mohammad Ghavamzadeh

AISTATS 2025 ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning Kaan Ozkara, Bruce Huang, Ruida Zhou, Suhas Diggavi

AISTATS 2025 Cost-Aware Optimal Pairwise Pure Exploration Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen

ICLR 2025 Data-Adaptive Differentially Private Prompt Synthesis for In-Context Learning Fengyu Gao, Ruida Zhou, Tianhao Wang, Cong Shen, Jing Yang

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

ICML 2025 On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures Wei Shen, Ruida Zhou, Jing Yang, Cong Shen

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

NeurIPSW 2024 Correlational Lagrangian Schrodinger Bridge: Learning Dynamics with Population-Level Regularization Yuning You, Ruida Zhou, Yang Shen

NeurIPSW 2024 From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization Qiang Zhang, Ruida Zhou, Yang Shen, Tie Liu

TMLR 2024 Harnessing the Power of Federated Learning in Federated Contextual Bandits Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen

ICLR 2024 Latent 3D Graph Diffusion Yuning You, Ruida Zhou, Jiwoong Park, Haotian Xu, Chao Tian, Zhangyang Wang, Yang Shen

ICML 2024 Path-Guided Particle-Based Sampling Mingzhou Fan, Ruida Zhou, Chao Tian, Xiaoning Qian

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

NeurIPSW 2024 Transformers Learn to Compress Variable-Order Markov Chains In-Context Ruida Zhou, Chao Tian, Suhas Diggavi

NeurIPS 2023 Federated Linear Bandits with Finite Adversarial Actions Li Fan, Ruida Zhou, Chao Tian, Cong Shen

NeurIPSW 2023 Harnessing the Power of Federated Learning in Federated Contextual Bandits Chengshuai Shi, Kun Yang, Ruida Zhou, Cong Shen

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

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

AISTATS 2022 Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances Ruida Zhou, Chao Tian

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

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

NeurIPS 2021 Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs Tao Liu, Ruida Zhou, Dileep Kalathil, Panganamala Kumar, Chao Tian

IJCAI 2018 Cost-Aware Cascading Bandits Ruida Zhou, Chao Gan, Jing Yang, Cong Shen

AISTATS 2018 Regional Multi-Armed Bandits Zhiyang Wang, Ruida Zhou, Cong Shen