Chiang, Chao-Kai

12 publications

AISTATS 2025 Domain Adaptation and Entanglement: An Optimal Transport Perspective Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama

TMLR 2025 Unified Risk Analysis for Weakly Supervised Learning Chao-Kai Chiang, Masashi Sugiyama

AAAI 2024 The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama

NeurIPS 2023 Imitation Learning from Vague Feedback Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama

ICML 2023 Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama

IJCAI 2019 Hyper-Parameter Tuning Under a Budget Constraint Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha

NeurIPS 2017 Federated Multi-Task Learning Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S Talwalkar

COLT 2016 An Algorithm with Nearly Optimal Pseudo-Regret for Both Stochastic and Adversarial Bandits Peter Auer, Chao-Kai Chiang

AISTATS 2016 Pareto Front Identification from Stochastic Bandit Feedback Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan

ACML 2014 Pseudo-Reward Algorithms for Contextual Bandits with Linear Payoff Functions Ku-Chun Chou, Hsuan-Tien Lin, Chao-Kai Chiang, Chi-Jen Lu

COLT 2013 Beating Bandits in Gradually Evolving Worlds Chao-Kai Chiang, Chia-Jung Lee, Chi-Jen Lu

COLT 2012 Online Optimization with Gradual Variations Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu