Toward Separating Integer Factoring from Discrete Logarithm mod p. - Shuji Isobe; Wataru Kumagai; Masahiro Mambo; Hiroki Shizuya (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2007)

Second order asymptotics for random number generation. - Wataru Kumagai; Masahito Hayashi (ISIT, 2013)

Second Order Asymptotics of Optimal Approximate Conversion for Probability Distributions and Entangled States and Its Application to LOCC Cloning. - Wataru Kumagai; Masahito Hayashi (CoRR, 2013)

Asymptotic reversibility of LOCC conversions. - Kosuke Ito; Wataru Kumagai; Masahito Hayashi (ISIT, 2014)

Random number conversion via restricted storage. - Wataru Kumagai; Masahito Hayashi (ISIT, 2014)

Search Dynamics Analysis and Adaptive Parameter Adjustment of Cuckoo Search. - Wataru Kumagai; Kenichi Tamura; Keiichiro Yasuda (SMC, 2015)

Asymptotic Compatibility between LOCC Conversion and Recovery. - Kosuke Ito; Wataru Kumagai; Masahito Hayashi (CoRR, 2015)

Learning Bound for Parameter Transfer Learning. - Wataru Kumagai (NIPS, 2016)

Regret Analysis for Continuous Dueling Bandit. - Wataru Kumagai (NIPS, 2017)

Parallel distributed block coordinate descent methods based on pairwise comparison oracle. - Kota Matsui; Wataru Kumagai; Takafumi Kanamori (J. Glob. Optim., 2017)

Second-Order Asymptotics of Conversions of Distributions and Entangled States Based on Rayleigh-Normal Probability Distributions. - Wataru Kumagai; Masahito Hayashi (IEEE Trans. Inf. Theory, 2017)

Random Number Conversion and LOCC Conversion via Restricted Storage. - Wataru Kumagai; Masahito Hayashi (IEEE Trans. Inf. Theory, 2017)

Introduction to Bandit Convex Optimization Algorithms. - Wataru Kumagai (ISITA, 2018)

Particle Swarm Optimization with Rotational Invariance Using Correlativity. - Wataru Kumagai; Keiichiro Yasuda (SMC, 2018)

Corrections to "Second-Order Asymptotics of Conversions of Distributions and Entangled States Based on Rayleigh-Normal Probability Distributions". - Wataru Kumagai; Masahito Hayashi (IEEE Trans. Inf. Theory, 2018)

Corrections to "Random Number Conversion and LOCC Conversion via Restricted Storage". - Wataru Kumagai; Masahito Hayashi (IEEE Trans. Inf. Theory, 2018)

Spectral Embedded Deep Clustering. - Yuichiro Wada; Shugo Miyamoto; Takumi Nakagama; Léo Andéol; Wataru Kumagai; Takafumi Kanamori (Entropy, 2019)

Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning. - Yuichiro Wada; Siqiang Su; Wataru Kumagai; Takafumi Kanamori (IEICE Trans. Inf. Syst., 2019)

Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. - Wataru Kumagai; Takafumi Kanamori (Mach. Learn., 2019)

Variable Selection for Nonparametric Learning with Power Series Kernels. - Kota Matsui; Wataru Kumagai; Kenta Kanamori; Mitsuaki Nishikimi; Takafumi Kanamori (Neural Comput., 2019)

Universal Approximation Theorem for Equivariant Maps by Group CNNs. - Wataru Kumagai; Akiyoshi Sannai (CoRR, 2020)

Group Equivariant Conditional Neural Processes. - Makoto Kawano; Wataru Kumagai; Akiyoshi Sannai; Yusuke Iwasawa; Yutaka Matsuo (ICLR, 2021)

Equivariant and Invariant Reynolds Networks. - Akiyoshi Sannai; Makoto Kawano; Wataru Kumagai (CoRR, 2021)

Uncertainty propagation for dropout-based Bayesian neural networks. - Yuki Mae; Wataru Kumagai; Takafumi Kanamori (Neural Networks, 2021)

Langevin Autoencoders for Learning Deep Latent Variable Models. - Shohei Taniguchi; Yusuke Iwasawa; Wataru Kumagai; Yutaka Matsuo (NeurIPS, 2022)

MOEA/D with Feasibility-based Weight Adjustment for Constrained Optimization. - Yusuke Yasuda; Wataru Kumagai; Kenichi Tamura; Keiichiro Yasuda (SSCI, 2022)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

A Switching Normalization Method in Decomposition-based Constraint Handling for Constrained Optimization. - Yusuke Yasuda; Hidetoki Kojima; Wataru Kumagai; Kenichi Tamura; Keiichiro Yasuda (SICE, 2023)

LPML: LLM-Prompting Markup Language for Mathematical Reasoning. - Ryutaro Yamauchi; Sho Sonoda; Akiyoshi Sannai; Wataru Kumagai (CoRR, 2023)

Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance. - Shiro Takagi; Ryutaro Yamauchi; Wataru Kumagai (CoRR, 2023)

A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees. - Toshinori Kitamura; Tadashi Kozuno; Masahiro Kato; Yuki Ichihara; Soichiro Nishimori; Akiyoshi Sannai; Sho Sonoda; Wataru Kumagai; Yutaka Matsuo (CoRR, 2024)

A Dataset for Evaluating LLM-based Evaluation Functions for Research Question Extraction Task. - Yuya Fujisaki; Shiro Takagi; Hideki Asoh; Wataru Kumagai (CoRR, 2024)

Invariant and Equivariant Reynolds Networks. - Akiyoshi Sannai; Makoto Kawano; Wataru Kumagai (J. Mach. Learn. Res., 2024)

Universal approximation with neural networks on function spaces. - Wataru Kumagai; Akiyoshi Sannai; Makoto Kawano (J. Exp. Theor. Artif. Intell., 2024)

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form. - Toshinori Kitamura; Tadashi Kozuno; Wataru Kumagai; Kenta Hoshino; Yohei Hosoe; Kazumi Kasaura; Masashi Hamaya; Paavo Parmas; Yutaka Matsuo (ICLR, 2025)

Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation. - Toshinori Kitamura; Arnob Ghosh; Tadashi Kozuno; Wataru Kumagai; Kazumi Kasaura; Kenta Hoshino; Yohei Hosoe; Yutaka Matsuo (CoRR, 2025)