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Journal Articles
- 2017
[j1]Chia-Jung Lee, Chao-Kai Chiang, Mu-En Wu:
Online Learning Problems against Dynamic Strategies in Gradually Evolving Worlds. J. Inf. Hiding Multim. Signal Process. 8(4): 869-879 (2017)
Conference and Workshop Papers
- 2024
- [c12]Jongyeong Lee
, Chao-Kai Chiang, Masashi Sugiyama:
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. AAAI 2024: 13383-13390 - 2023
- [c11]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML 2023: 18810-18851 - [c10]Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama:
Imitation Learning from Vague Feedback. NeurIPS 2023 - 2019
- [c9]Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha:
Hyper-parameter Tuning under a Budget Constraint. IJCAI 2019: 5744-5750 - 2017
- [c8]Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar:
Federated Multi-Task Learning. NIPS 2017: 4424-4434 - 2016
- [c7]Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan:
Pareto Front Identification from Stochastic Bandit Feedback. AISTATS 2016: 939-947 - [c6]Peter Auer, Chao-Kai Chiang:
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits. COLT 2016: 116-120 - 2015
- [c5]Chia-Jung Lee, Chao-Kai Chiang, Mu-En Wu:
Resisting Dynamic Strategies in Gradually Evolving Worlds. RVSP 2015: 191-194 - 2014
- [c4]Ku-Chun Chou, Hsuan-Tien Lin, Chao-Kai Chiang, Chi-Jen Lu:
Pseudo-reward Algorithms for Contextual Bandits with Linear Payoff Functions. ACML 2014 - 2013
- [c3]Chao-Kai Chiang, Chia-Jung Lee, Chi-Jen Lu:
Beating Bandits in Gradually Evolving Worlds. COLT 2013: 210-227 - 2012
- [c2]Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu:
Online Optimization with Gradual Variations. COLT 2012: 6.1-6.20 - 2010
- [c1]Chao-Kai Chiang, Chi-Jen Lu:
Online Learning with Queries. SODA 2010: 616-629
Informal and Other Publications
- 2023
- [i6]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. CoRR abs/2302.01544 (2023) - [i5]Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama:
Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. CoRR abs/2302.14407 (2023) - [i4]Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. CoRR abs/2309.08216 (2023) - 2019
- [i3]Zhiyun Lu, Chao-Kai Chiang, Fei Sha:
Hyper-parameter Tuning under a Budget Constraint. CoRR abs/1902.00532 (2019) - 2017
- [i2]Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar:
Federated Multi-Task Learning. CoRR abs/1705.10467 (2017) - 2016
- [i1]Peter Auer, Chao-Kai Chiang:
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits. CoRR abs/1605.08722 (2016)
Coauthor Index
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