A Parallel and Accelerated Circuit Simulator with Precise Accuracy. - Peter M. Lee; Shinji Ito; Takeaki Hashimoto; Junji Sato; Tomomasa Touma; Goichi Yokomizo (ASP-DAC/VLSI Design, 2002)

Orthogonal polynomial approach to estimation of poles of rational functions from data on open curves. - Shinji Ito; Kensuke Aishima; Takaaki Nara; Masaaki Sugihara (J. Comput. Appl. Math., 2015)

Supporting System for Descriptive Quiz in Large Class - Effectiveness of the Three-step-view System -. - Shinji Ito; Tomoya Oba; Haruhiko Takase; Hiroharu Kawanaka; Shinji Tsuruoka (KES, 2016)

Large-Scale Price Optimization via Network Flow. - Shinji Ito; Ryohei Fujimaki (NIPS, 2016)

An Algorithm for the Generalized Eigenvalue Problem for Nonsquare Matrix Pencils by Minimal Perturbation Approach. - Shinji Ito; Kazuo Murota (SIAM J. Matrix Anal. Appl., 2016)

Robust Quadratic Programming for Price Optimization. - Akihiro Yabe; Shinji Ito; Ryohei Fujimaki (IJCAI, 2017)

Optimization Beyond Prediction: Prescriptive Price Optimization. - Shinji Ito; Ryohei Fujimaki (KDD, 2017)

Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Akihiro Yabe; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NIPS, 2017)

Online Regression with Partial Information: Generalization and Linear Projection. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Akihiro Yabe; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AISTATS, 2018)

Unbiased Objective Estimation in Predictive Optimization. - Shinji Ito; Akihiro Yabe; Ryohei Fujimaki (ICML, 2018)

Causal Bandits with Propagating Inference. - Akihiro Yabe; Daisuke Hatano; Hanna Sumita; Shinji Ito; Naonori Kakimura; Takuro Fukunaga; Ken-ichi Kawarabayashi (ICML, 2018)

Regret Bounds for Online Portfolio Selection with a Cardinality Constraint. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Akihiro Yabe; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2018)

Compressed Vector Set: A Fast and Space-Efficient Data Mining Framework. - Masafumi Oyamada; Jianquan Liu; Shinji Ito; Kazuyo Narita; Takuya Araki; Hiroyuki Kitagawa (J. Inf. Process., 2018)

Stable polefinding and rational least-squares fitting via eigenvalues. - Shinji Ito; Yuji Nakatsukasa (Numerische Mathematik, 2018)

An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits. - Kei Takemura; Shinji Ito (ICDM, 2019)

Submodular Function Minimization with Noisy Evaluation Oracle. - Shinji Ito (NeurIPS, 2019)

Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2019)

Improved Regret Bounds for Bandit Combinatorial Optimization. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2019)

An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss. - Shinji Ito (AISTATS, 2020)

A Tight Lower Bound and Efficient Reduction for Swap Regret. - Shinji Ito (NeurIPS, 2020)

Delay and Cooperation in Nonstochastic Linear Bandits. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2020)

Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits. - Shinji Ito; Shuichi Hirahara; Tasuku Soma; Yuichi Yoshida (NeurIPS, 2020)

Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions. - Kei Takemura; Shinji Ito; Daisuke Hatano; Hanna Sumita; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AAAI, 2021)

Tracking Regret Bounds for Online Submodular Optimization. - Tatsuya Matsuoka; Shinji Ito; Naoto Ohsaka (AISTATS, 2021)

A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits. - Kei Takemura; Shinji Ito; Daisuke Hatano; Hanna Sumita; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AISTATS, 2021)

Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds. - Shinji Ito (COLT, 2021)

Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits. - Shinji Ito (NeurIPS, 2021)

On Optimal Robustness to Adversarial Corruption in Online Decision Problems. - Shinji Ito (NeurIPS, 2021)

Online Task Assignment Problems with Reusable Resources. - Hanna Sumita; Shinji Ito; Kei Takemura; Daisuke Hatano; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AAAI, 2022)

Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. - Shinji Ito; Taira Tsuchiya; Junya Honda (COLT, 2022)

Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness. - Shinji Ito (ICML, 2022)

Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. - Shinji Ito; Taira Tsuchiya; Junya Honda (NeurIPS, 2022)

Single Loop Gaussian Homotopy Method for Non-convex Optimization. - Hidenori Iwakiri; Yuhang Wang; Shinji Ito; Akiko Takeda (NeurIPS, 2022)

Average Sensitivity of Euclidean k-Clustering. - Yuichi Yoshida; Shinji Ito (NeurIPS, 2022)

Maximization of Minimum Weighted Hamming Distance between Set Pairs. - Tatsuya Matsuoka; Shinji Ito (ACML, 2023)

Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. - Taira Tsuchiya; Shinji Ito; Junya Honda (AISTATS, 2023)

Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems. - Junya Honda; Shinji Ito; Taira Tsuchiya (ALT, 2023)

Best-of-Both-Worlds Algorithms for Partial Monitoring. - Taira Tsuchiya; Shinji Ito; Junya Honda (ALT, 2023)

Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds. - Shinji Ito; Kei Takemura (COLT, 2023)

Bandit Task Assignment with Unknown Processing Time. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2023)

An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits. - Shinji Ito; Kei Takemura (NeurIPS, 2023)

Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds. - Taira Tsuchiya; Shinji Ito; Junya Honda (NeurIPS, 2023)

New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem. - Koji Ichikawa; Shinji Ito; Daisuke Hatano; Hanna Sumita; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AAAI, 2024)

Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. - Shinji Ito; Taira Tsuchiya; Junya Honda (COLT, 2024)

Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds. - Jongyeong Lee; Junya Honda; Shinji Ito; Min-hwan Oh (COLT, 2024)

Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. - Taira Tsuchiya; Shinji Ito; Junya Honda (ICML, 2024)

Learning with Posterior Sampling for Revenue Management under Time-varying Demand. - Kazuma Shimizu; Junya Honda; Shinji Ito; Shinji Nakadai (IJCAI, 2024)

On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice. - Shinji Ito (NeurIPS, 2024)

A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $ Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds. - Taira Tsuchiya; Shinji Ito (NeurIPS, 2024)

Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets. - Taira Tsuchiya; Shinji Ito (NeurIPS, 2024)

Online $ textrm{L}^{ atural }$-Convex Minimization. - Ken Yokoyama; Shinji Ito; Tatsuya Matsuoka; Kei Kimura; Makoto Yokoo (ECML/PKDD (5), 2024)

Replicability is Asymptotically Free in Multi-armed Bandits. - Junpei Komiyama; Shinji Ito; Yuichi Yoshida; Souta Koshino (CoRR, 2024)

Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets. - Taira Tsuchiya; Shinji Ito (CoRR, 2024)

LC-Tsalis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits. - Masahiro Kato; Shinji Ito (CoRR, 2024)

Online L♮-Convex Minimization. - Ken Yokoyama; Shinji Ito; Tatsuya Matsuoka; Kei Kimura; Makoto Yokoo (CoRR, 2024)

A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of θ(T2/3) and its Application to Best-of-Both-Worlds. - Taira Tsuchiya; Shinji Ito (CoRR, 2024)

Contaminated Online Convex Optimization. - Tomoya Kamijima; Shinji Ito (Trans. Mach. Learn. Res., 2024)

Best-of-Both-Worlds Linear Contextual Bandits. - Masahiro Kato; Shinji Ito (Trans. Mach. Learn. Res., 2024)

Active model selection: A variance minimization approach. - Satoshi Hara 001; Mitsuru Matsuura; Junya Honda; Shinji Ito (Mach. Learn., 2024)

LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits. - Masahiro Kato; Shinji Ito (AISTATS, 2025)

Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback. - Shinji Ito; Haipeng Luo; Taira Tsuchiya; Yue Wu (COLT, 2025)

Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. - Quan Nguyen; Shinji Ito; Junpei Komiyama; Nishant Mehta (COLT, 2025)

Corrupted Learning Dynamics in Games. - Taira Tsuchiya; Shinji Ito; Haipeng Luo (COLT, 2025)

Influential Bandits: Pulling an Arm May Change the Environment. - Ryoma Sato; Shinji Ito (Trans. Mach. Learn. Res., 2025)

Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. - Quan Nguyen; Shinji Ito; Junpei Komiyama; Nishant A. Mehta (CoRR, 2025)

Bandit Max-Min Fair Allocation. - Tsubasa Harada; Shinji Ito; Hanna Sumita (CoRR, 2025)

Optimal Regret of Bernoulli Bandits under Global Differential Privacy. - Achraf Azize; Yulian Wu; Junya Honda; Francesco Orabona; Shinji Ito; Debabrota Basu (CoRR, 2025)

Reinforcement Learning from Adversarial Preferences in Tabular MDPs. - Taira Tsuchiya; Shinji Ito; Haipeng Luo (CoRR, 2025)