Expectation Propagation for t-Exponential Family Using q-Algebra. - Futoshi Futami; Issei Sato; Masashi Sugiyama (NIPS, 2017)

Variational Inference based on Robust Divergences. - Futoshi Futami; Issei Sato; Masashi Sugiyama (AISTATS, 2018)

Frank-Wolfe Stein Sampling. - Futoshi Futami; Zhenghang Cui; Issei Sato; Masashi Sugiyama (CoRR, 2018)

Bayesian Posterior Approximation via Greedy Particle Optimization. - Futoshi Futami; Zhenghang Cui; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. - Futoshi Futami; Issei Sato; Masashi Sugiyama (ICML, 2020)

Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. - Hideaki Imamura; Nontawat Charoenphakdee; Futoshi Futami; Issei Sato; Junya Honda; Masashi Sugiyama (CoRR, 2020)

Scalable gradient matching based on state space Gaussian Processes. - Futoshi Futami (ACML, 2021)

Skew-symmetrically perturbed gradient flow for convex optimization. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Ikko Yamane (ACML, 2021)

Loss function based second-order Jensen inequality and its application to particle variational inference. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (NeurIPS, 2021)

Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato (Entropy, 2021)

Predictive variational Bayesian inference as risk-seeking optimization. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (AISTATS, 2022)

Excess risk analysis for epistemic uncertainty with application to variational inference. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Issei Sato; Masashi Sugiyama (CoRR, 2022)

Time-Independent Information-Theoretic Generalization Bounds for SGLD. - Futoshi Futami; Masahiro Fujisawa (NeurIPS, 2023)

Information-theoretic Analysis of Test Data Sensitivity in Uncertainty. - Futoshi Futami; Tomoharu Iwata (CoRR, 2023)

Information-theoretic Analysis of Bayesian Test Data Sensitivity. - Futoshi Futami; Tomoharu Iwata (AISTATS, 2024)

Information-theoretic Generalization Analysis for Expected Calibration Error. - Futoshi Futami; Masahiro Fujisawa (NeurIPS, 2024)

PAC-Bayes Analysis for Recalibration in Classification. - Masahiro Fujisawa; Futoshi Futami (CoRR, 2024)

Epistemic Uncertainty and Excess Risk in Variational Inference. - Futoshi Futami (AISTATS, 2025)

On the Convergence of SVGD in KL divergence via Approximate gradient flow. - Masahiro Fujisawa; Futoshi Futami (Trans. Mach. Learn. Res., 2025)

Uniform convergence of the smooth calibration error and its relationship with functional gradient. - Futoshi Futami; Atsushi Nitanda (CoRR, 2025)

Information-theoretic Generalization Analysis for VQ-VAEs: A Role of Latent Variables. - Futoshi Futami; Masahiro Fujisawa (CoRR, 2025)