| 2025 | AISTATS | Domain Adaptation and Entanglement: an Optimal Transport Perspective. | Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama |
| 2024 | AAAI | The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. | Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama |
| 2023 | ICML | Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. | Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama |
| 2019 | IJCAI | Hyper-parameter Tuning under a Budget Constraint. | Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha |
| 2016 | AISTATS | Pareto Front Identification from Stochastic Bandit Feedback. | Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan |
| 2016 | COLT | An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits. | Peter Auer, Chao-Kai Chiang |
| 2014 | ACML | Pseudo-reward Algorithms for Contextual Bandits with Linear Payoff Functions. | Ku-Chun Chou, Hsuan-Tien Lin, Chao-Kai Chiang, Chi-Jen Lu |
| 2013 | COLT | Beating Bandits in Gradually Evolving Worlds. | Chao-Kai Chiang, Chia-Jung Lee, Chi-Jen Lu |
| 2010 | SODA | Online Learning with Queries. | Chao-Kai Chiang, Chi-Jen Lu |