Multi-level Monte Carlo Variational Inference. - Masahiro Fujisawa; Issei Sato (CoRR, 2019)

γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato (CoRR, 2020)

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato; Masashi Sugiyama (AISTATS, 2021)

Multilevel Monte Carlo Variational Inference. - Masahiro Fujisawa; Issei Sato (J. Mach. Learn. Res., 2021)

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

Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos. - Takeshi Koshizuka; Masahiro Fujisawa; Yusuke Tanaka; Issei Sato (CoRR, 2023)

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

Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective. - Takeshi Koshizuka; Masahiro Fujisawa; Yusuke Tanaka; Issei Sato (NeurIPS, 2024)

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

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

Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature. - Masaki Adachi; Masahiro Fujisawa; Michael A. Osborne (CoRR, 2025)

Variational OOD State Correction for Offline Reinforcement Learning. - Ke Jiang; Wen Jiang; Masahiro Fujisawa; Xiaoyang Tan (CoRR, 2025)

Scalable Valuation of Human Feedback through Provably Robust Model Alignment. - Masahiro Fujisawa; Masaki Adachi; Michael A. Osborne (CoRR, 2025)

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