no code implementations • 23 Feb 2024 • Junwen Yang, Tianyuan Jin, Vincent Y. F. Tan
Our results offer valuable quantitative insights into the benefits of the abstention option, laying the groundwork for further exploration in other online decision-making problems with such an option.
1 code implementation • 24 Dec 2023 • Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu
Specifically, we assume there is a local reward for each hyperedge, and the reward of the joint arm is the sum of these local rewards.
no code implementations • 21 Oct 2023 • Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu
Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i. e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero.
no code implementations • 7 Jun 2022 • Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar
We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distributions including Bernoulli, Gaussian, Gamma, Exponential, etc.
no code implementations • 3 Mar 2020 • Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
Thompson sampling is one of the most widely used algorithms for many online decision problems, due to its simplicity in implementation and superior empirical performance over other state-of-the-art methods.
no code implementations • 21 Feb 2020 • Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
In this paper, we show that a variant of ETC algorithm can actually achieve the asymptotic optimality for multi-armed bandit problems as UCB-type algorithms do and extend it to the batched bandit setting.
no code implementations • 19 Feb 2020 • Jieming Shi, Tianyuan Jin, Renchi Yang, Xiaokui Xiao, Yin Yang
Given a graph G and a node u in G, a single source SimRank query evaluates the similarity between u and every node v in G. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which needs to be performed again whenever the graph G changes.
1 code implementation • NeurIPS 2019 • Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen
For PAC problem, we achieve optimal query complexity and use only $O(\log_{\frac{k}{\delta}}^*(n))$ rounds, which matches the lower bound of round complexity, while most of existing works need $\Theta(\log \frac{n}{k})$ rounds.