Futoshi Futami

Mach. Learn., May, 2026

Information-Theoretic Generalization Bounds for Sequential Decision Making.

[BibT_eX]

[DOI]

CoRR, May, 2026

Unified Approach for Weakly Supervised Multicalibration.

[BibT_eX]

[DOI]

Takashi Ishida

CoRR, May, 2026

Robust and Computationally Efficient Linear Contextual Bandits under Adversarial Corruption and Heavy-Tailed Noise.

[BibT_eX]

[DOI]

Naoto Tani

CoRR, March, 2026

Estimating Expected Calibration Error for Positive-Unlabeled Learning.

[BibT_eX]

[DOI]

Ryuichi Kiryo

Trans. Mach. Learn. Res., 2026

2025

L<sub>2</sub>-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error.

[BibT_eX]

[DOI]

CoRR, October, 2025

Information-theoretic Generalization Analysis for VQ-VAEs: A Role of Latent Variables.

[BibT_eX]

[DOI]

CoRR, May, 2025

Uniform convergence of the smooth calibration error and its relationship with functional gradient.

[BibT_eX]

[DOI]

Atsushi Nitanda

CoRR, May, 2025

On the Convergence of SVGD in KL divergence via Approximate gradient flow.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2025

PAC-Bayes Analysis for Recalibration in Classification.

[BibT_eX]

[DOI]

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Epistemic Uncertainty and Excess Risk in Variational Inference.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Information-theoretic Generalization Analysis for Expected Calibration Error.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Information-theoretic Analysis of Bayesian Test Data Sensitivity.

[BibT_eX]

[DOI]

Tomoharu Iwata

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Information-theoretic Analysis of Test Data Sensitivity in Uncertainty.

[BibT_eX]

[DOI]

Tomoharu Iwata

CoRR, 2023

Time-Independent Information-Theoretic Generalization Bounds for SGLD.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Excess risk analysis for epistemic uncertainty with application to variational inference.

[BibT_eX]

[DOI]

CoRR, 2022

Predictive variational Bayesian inference as risk-seeking optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices.

[BibT_eX]

[DOI]

Entropy, 2021

Loss function based second-order Jensen inequality and its application to particle variational inference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Skew-symmetrically perturbed gradient flow for convex optimization.

[BibT_eX]

[DOI]

Proceedings of the Asian Conference on Machine Learning, 2021

Scalable gradient matching based on state space Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time.

[BibT_eX]

[DOI]

Hideaki Imamura

Nontawat Charoenphakdee

CoRR, 2020

Accelerating the diffusion-based ensemble sampling by non-reversible dynamics.

[BibT_eX]

[DOI]

Issei Sato

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Bayesian Posterior Approximation via Greedy Particle Optimization.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Frank-Wolfe Stein Sampling.

[BibT_eX]

[DOI]

CoRR, 2018

Variational Inference based on Robust Divergences.

[BibT_eX]

[DOI]

Issei Sato

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Expectation Propagation for t-Exponential Family Using q-Algebra.

[BibT_eX]

[DOI]

Issei Sato