Search Results for author: Shinji Ito

Found 20 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 $i$.

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

Online Task Assignment Problems with Reusable Resources

no code implementations15 Mar 2022 Hanna Sumita, Shinji Ito, Kei Takemura, Daisuke Hatano, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

The key features of our problem are (1) an agent is reusable, i. e., an agent comes back to the market after completing the assigned task, (2) an agent may reject the assigned task to stay the market, and (3) a task may accommodate multiple agents.

Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits

no code implementations NeurIPS 2021 Shinji Ito

This study aims to develop bandit algorithms that automatically exploit tendencies of certain environments to improve performance, without any prior knowledge regarding the environments.

On Optimal Robustness to Adversarial Corruption in Online Decision Problems

no code implementations NeurIPS 2021 Shinji Ito

The main contribution of this paper is to show that optimal robustness can be expressed by a square-root dependency on the amount of corruption.

Decision Making

Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions

no code implementations20 Jan 2021 Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

However, there is a gap of $\tilde{O}(\max(\sqrt{d}, \sqrt{k}))$ between the current best upper and lower bounds, where $d$ is the dimension of the feature vectors, $k$ is the number of the chosen arms in a round, and $\tilde{O}(\cdot)$ ignores the logarithmic factors.

Decision Making Recommendation Systems

A Tight Lower Bound and Efficient Reduction for Swap Regret

no code implementations NeurIPS 2020 Shinji Ito

Swap regret, a generic performance measure of online decision-making algorithms, plays an important role in the theory of repeated games, along with a close connection to correlated equilibria in strategic games.

Decision Making

Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits

no code implementations NeurIPS 2020 Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida

We propose novel algorithms with first- and second-order regret bounds for adversarial linear bandits.

Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback

no code implementations NeurIPS 2019 Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

Our algorithm for non-stochastic settings has an oracle complexity of $\tilde{O}( T )$ and is the first algorithm that achieves both a regret bound of $\tilde{O}( \sqrt{T} )$ and an oracle complexity of $\tilde{O} ( \mathrm{poly} ( T ) )$, given only linear optimization oracles.

Submodular Function Minimization with Noisy Evaluation Oracle

no code implementations NeurIPS 2019 Shinji Ito

This paper considers submodular function minimization with \textit{noisy evaluation oracles} that return the function value of a submodular objective with zero-mean additive noise.

Improved Regret Bounds for Bandit Combinatorial Optimization

no code implementations NeurIPS 2019 Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

\textit{Bandit combinatorial optimization} is a bandit framework in which a player chooses an action within a given finite set $\mathcal{A} \subseteq \{ 0, 1 \}^d$ and incurs a loss that is the inner product of the chosen action and an unobservable loss vector in $\mathbb{R} ^ d$ in each round.

Combinatorial Optimization

An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits

no code implementations5 Sep 2019 Kei Takemura, Shinji Ito

Our empirical evaluation with artificial and real-world datasets demonstrates that the proposed algorithms with the arm-wise randomization technique outperform the existing algorithms without this technique, especially for the clustered case.

Decision Making Recommendation Systems

Regret Bounds for Online Portfolio Selection with a Cardinality Constraint

no code implementations NeurIPS 2018 Shinji Ito, Daisuke Hatano, Sumita Hanna, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi

Online portfolio selection is a sequential decision-making problem in which a learner repetitively selects a portfolio over a set of assets, aiming to maximize long-term return.

Decision Making

Unbiased Objective Estimation in Predictive Optimization

no code implementations ICML 2018 Shinji Ito, Akihiro Yabe, Ryohei Fujimaki

Predictive optimization, however, suffers from the problem of a calculated optimal solution’s being evaluated too optimistically, i. e., the value of the objective function is overestimated.

Decision Making

Causal Bandits with Propagating Inference

no code implementations ICML 2018 Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi

In this setting, the arms are identified with interventions on a given causal graph, and the effect of an intervention propagates throughout all over the causal graph.

Large-Scale Price Optimization via Network Flow

no code implementations NeurIPS 2016 Shinji Ito, Ryohei Fujimaki

On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms.

Optimization Beyond Prediction: Prescriptive Price Optimization

no code implementations18 May 2016 Shinji Ito, Ryohei Fujimaki

This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning.

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