Search Results for author: Taira Tsuchiya

Found 6 papers, 0 papers with code

Best-of-Both-Worlds Algorithms for Partial Monitoring

no code implementations29 Jul 2022 Taira Tsuchiya, Shinji Ito, Junya Honda

To be more specific, we show that for non-degenerate locally observable games, the regret in the stochastic regime is bounded by $O(k^3 m^2 \log(T) \log(k_{\Pi} T) / \Delta_{\mathrm{\min}})$ and in the adversarial regime by $O(k^{2/3} m \sqrt{T \log(T) \log k_{\Pi}})$, where $T$ is the number of rounds, $m$ is the maximum number of distinct observations per action, $\Delta_{\min}$ is the minimum optimality gap, and $k_{\Pi}$ is the number of Pareto optimal actions.

online learning

Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds

no code implementations14 Jun 2022 Shinji Ito, Taira Tsuchiya, Junya Honda

In fact, they have provided a stochastic MAB algorithm with gap-variance-dependent regret bounds of $O(\sum_{i: \Delta_i>0} (\frac{\sigma_i^2}{\Delta_i} + 1) \log T )$ for loss variance $\sigma_i^2$ of arm $i$.

Globally Optimal Algorithms for Fixed-Budget Best Arm Identification

no code implementations9 Jun 2022 Junpei Komiyama, Taira Tsuchiya, Junya Honda

We consider the fixed-budget best arm identification problem where the goal is to find the arm of the largest mean with a fixed number of samples.

Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs

no code implementations2 Jun 2022 Shinji Ito, Taira Tsuchiya, Junya Honda

As Alon et al. [2015] have shown, tight regret bounds depend on the structure of the feedback graph: \textit{strongly observable} graphs yield minimax regret of $\tilde{\Theta}( \alpha^{1/2} T^{1/2} )$, while \textit{weakly observable} graphs induce minimax regret of $\tilde{\Theta}( \delta^{1/3} T^{2/3} )$, where $\alpha$ and $\delta$, respectively, represent the independence number of the graph and the domination number of a certain portion of the graph.

online learning

Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring

no code implementations NeurIPS 2020 Taira Tsuchiya, Junya Honda, Masashi Sugiyama

We investigate finite stochastic partial monitoring, which is a general model for sequential learning with limited feedback.

Decision Making

Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization

no code implementations31 Jan 2019 Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama

We further provide an estimation error bound to show that our risk estimator is consistent.

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