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Shinji Ito
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Journal Articles
- 2024
- [j5]Masahiro Kato, Shinji Ito:
Best-of-Both-Worlds Linear Contextual Bandits. Trans. Mach. Learn. Res. 2024 (2024) - 2018
- [j4]Masafumi Oyamada, Jianquan Liu, Shinji Ito, Kazuyo Narita, Takuya Araki, Hiroyuki Kitagawa
:
Compressed Vector Set: A Fast and Space-Efficient Data Mining Framework. J. Inf. Process. 26: 416-426 (2018) - [j3]Shinji Ito, Yuji Nakatsukasa
Stable polefinding and rational least-squares fitting via eigenvalues. Numerische Mathematik 139(3): 633-682 (2018) - 2016
- [j2]Shinji Ito, Kazuo Murota:
An Algorithm for the Generalized Eigenvalue Problem for Nonsquare Matrix Pencils by Minimal Perturbation Approach. SIAM J. Matrix Anal. Appl. 37(1): 409-419 (2016) - 2015
- [j1]Shinji Ito, Kensuke Aishima
Orthogonal polynomial approach to estimation of poles of rational functions from data on open curves. J. Comput. Appl. Math. 273: 326-345 (2015)
Conference and Workshop Papers
- 2024
- [c44]Koji Ichikawa, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem. AAAI 2024: 12708-12716 - [c43]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. COLT 2024: 2522-2563 - [c42]Jongyeong Lee, Junya Honda, Shinji Ito, Min-hwan Oh:
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds. COLT 2024: 3375-3430 - [c41]Taira Tsuchiya, Shinji Ito, Junya Honda:
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. ICML 2024 - [c40]Kazuma Shimizu, Junya Honda, Shinji Ito, Shinji Nakadai:
Learning with Posterior Sampling for Revenue Management under Time-varying Demand. IJCAI 2024: 4911-4919 - [c39]Ken Yokoyama, Shinji Ito, Tatsuya Matsuoka, Kei Kimura, Makoto Yokoo:
Online $\textrm{L}^{\natural }$-Convex Minimization. ECML/PKDD (5) 2024: 319-336 - 2023
- [c38]Tatsuya Matsuoka, Shinji Ito:
Maximization of Minimum Weighted Hamming Distance between Set Pairs. ACML 2023: 895-910 - [c37]Taira Tsuchiya, Shinji Ito, Junya Honda:
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. AISTATS 2023: 8117-8144 - [c36]Junya Honda, Shinji Ito, Taira Tsuchiya:
Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems. ALT 2023: 726-754 - [c35]Taira Tsuchiya, Shinji Ito, Junya Honda:
Best-of-Both-Worlds Algorithms for Partial Monitoring. ALT 2023: 1484-1515 - [c34]Shinji Ito, Kei Takemura:
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds. COLT 2023: 2653-2677 - [c33]Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Bandit Task Assignment with Unknown Processing Time. NeurIPS 2023 - [c32]Shinji Ito, Kei Takemura:
An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits. NeurIPS 2023 - [c31]Taira Tsuchiya, Shinji Ito, Junya Honda:
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds. NeurIPS 2023 - 2022
- [c30]Hanna Sumita, Shinji Ito, Kei Takemura, Daisuke Hatano, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Online Task Assignment Problems with Reusable Resources. AAAI 2022: 5199-5207 - [c29]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. COLT 2022: 1421-1422 - [c28]Shinji Ito:
Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness. ICML 2022: 9678-9694 - [c27]Shinji Ito, Taira Tsuchiya, Junya Honda:
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. NeurIPS 2022 - [c26]Hidenori Iwakiri, Yuhang Wang, Shinji Ito, Akiko Takeda:
Single Loop Gaussian Homotopy Method for Non-convex Optimization. NeurIPS 2022 - [c25]Yuichi Yoshida, Shinji Ito:
Average Sensitivity of Euclidean k-Clustering. NeurIPS 2022 - 2021
- [c24]Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions. AAAI 2021: 9791-9798 - [c23]Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits. AISTATS 2021: 3367-3375 - [c22]Tatsuya Matsuoka, Shinji Ito, Naoto Ohsaka:
Tracking Regret Bounds for Online Submodular Optimization. AISTATS 2021: 3421-3429 - [c21]Shinji Ito:
Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds. COLT 2021: 2552-2583 - [c20]Shinji Ito:
Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits. NeurIPS 2021: 2654-2667 - [c19]Shinji Ito:
On Optimal Robustness to Adversarial Corruption in Online Decision Problems. NeurIPS 2021: 7409-7420 - 2020
- [c18]Shinji Ito:
An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss. AISTATS 2020: 2229-2239 - [c17]Shinji Ito:
A Tight Lower Bound and Efficient Reduction for Swap Regret. NeurIPS 2020 - [c16]Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Delay and Cooperation in Nonstochastic Linear Bandits. NeurIPS 2020 - [c15]Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida:
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits. NeurIPS 2020 - 2019
- [c14]Kei Takemura, Shinji Ito:
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits. ICDM 2019: 1318-1323 - [c13]Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback. NeurIPS 2019: 10589-10598 - [c12]Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Improved Regret Bounds for Bandit Combinatorial Optimization. NeurIPS 2019: 12027-12036 - [c11]Shinji Ito:
Submodular Function Minimization with Noisy Evaluation Oracle. NeurIPS 2019: 12080-12090 - 2018
- [c10]Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Online Regression with Partial Information: Generalization and Linear Projection. AISTATS 2018: 1599-1607 - [c9]Shinji Ito, Akihiro Yabe, Ryohei Fujimaki:
Unbiased Objective Estimation in Predictive Optimization. ICML 2018: 2181-2190 - [c8]Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi:
Causal Bandits with Propagating Inference. ICML 2018: 5508-5516 - [c7]Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint. NeurIPS 2018: 10611-10620 - 2017
- [c6]Akihiro Yabe, Shinji Ito, Ryohei Fujimaki:
Robust Quadratic Programming for Price Optimization. IJCAI 2017: 4648-4654 - [c5]Shinji Ito, Ryohei Fujimaki:
Optimization Beyond Prediction: Prescriptive Price Optimization. KDD 2017: 1833-1841 - [c4]Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation. NIPS 2017: 4099-4108 - 2016
- [c3]Shinji Ito, Tomoya Oba, Haruhiko Takase, Hiroharu Kawanaka, Shinji Tsuruoka:
Supporting System for Descriptive Quiz in Large Class - Effectiveness of the Three-step-view System -. KES 2016: 1166-1171 - [c2]Shinji Ito, Ryohei Fujimaki:
Large-Scale Price Optimization via Network Flow. NIPS 2016: 3855-3863 - 2002
- [c1]Peter M. Lee, Shinji Ito, Takeaki Hashimoto, Junji Sato, Tomomasa Touma, Goichi Yokomizo:
A Parallel and Accelerated Circuit Simulator with Precise Accuracy. ASP-DAC/VLSI Design 2002: 213-218
Informal and Other Publications
- 2024
- [i22]Junpei Komiyama, Shinji Ito, Yuichi Yoshida, Souta Koshino:
Replicability is Asymptotically Free in Multi-armed Bandits. CoRR abs/2402.07391 (2024) - [i21]Taira Tsuchiya, Shinji Ito, Junya Honda:
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. CoRR abs/2402.08321 (2024) - [i20]Taira Tsuchiya, Shinji Ito:
Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets. CoRR abs/2402.12868 (2024) - [i19]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. CoRR abs/2403.00715 (2024) - [i18]Masahiro Kato, Shinji Ito:
LC-Tsalis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits. CoRR abs/2403.03219 (2024) - [i17]Jongyeong Lee
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds. CoRR abs/2403.05134 (2024) - [i16]Ken Yokoyama, Shinji Ito, Tatsuya Matsuoka, Kei Kimura, Makoto Yokoo:
Online L♮-Convex Minimization. CoRR abs/2404.17158 (2024) - [i15]Kazuma Shimizu, Junya Honda, Shinji Ito, Shinji Nakadai:
Learning with Posterior Sampling for Revenue Management under Time-varying Demand. CoRR abs/2405.04910 (2024) - [i14]Taira Tsuchiya, Shinji Ito:
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. CoRR abs/2405.20028 (2024) - 2023
- [i13]Shinji Ito, Kei Takemura:
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds. CoRR abs/2302.12370 (2023) - [i12]Taira Tsuchiya, Shinji Ito, Junya Honda:
Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds. CoRR abs/2305.17301 (2023) - [i11]Koji Ichikawa, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
New classes of the greedy-applicable arm feature distributions in the sparse linear bandit problem. CoRR abs/2312.12400 (2023) - [i10]Masahiro Kato, Shinji Ito:
Best-of-Both-Worlds Linear Contextual Bandits. CoRR abs/2312.16489 (2023) - 2022
- [i9]Hanna Sumita, Shinji Ito, Kei Takemura, Daisuke Hatano, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Online Task Assignment Problems with Reusable Resources. CoRR abs/2203.07605 (2022) - [i8]Shinji Ito, Taira Tsuchiya, Junya Honda:
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. CoRR abs/2206.00873 (2022) - [i7]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. CoRR abs/2206.06810 (2022) - [i6]Taira Tsuchiya, Shinji Ito, Junya Honda:
Best-of-Both-Worlds Algorithms for Partial Monitoring. CoRR abs/2207.14550 (2022) - 2021
- [i5]Kei Takemura, Shinji Ito, Daisuke Hatano, Hanna Sumita, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions. CoRR abs/2101.07957 (2021) - [i4]Shinji Ito:
On Optimal Robustness to Adversarial Corruption in Online Decision Problems. CoRR abs/2109.10963 (2021) - 2019
- [i3]Kei Takemura, Shinji Ito:
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits. CoRR abs/1909.02251 (2019) - 2018
- [i2]Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi:
Causal Bandits with Propagating Inference. CoRR abs/1806.02252 (2018) - 2016
- [i1]Shinji Ito, Ryohei Fujimaki:
Optimization Beyond Prediction: Prescriptive Price Optimization. CoRR abs/1605.05422 (2016)
Coauthor Index
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