112 citations · 162 across the 4 of their papers we have counts for
4 papers
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
Jongyeong Lee, Junya Honda, Chao-Kai Chiang +1
In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In ad…
Hyper-parameter Tuning under a Budget Constraint
Zhiyun Lu, Chao-Kai Chiang, Fei Sha
We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint. We propose to solve it as a sequential decision making pro…
Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi +1
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task lear…
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
Peter Auer, Chao-Kai Chiang
We present an algorithm that achieves almost optimal pseudo-regret bounds against adversarial and stochastic bandits. Against adversarial bandits the pseudo-regret is $O(K\sqrt{n \…