2 citations · 3 across the 9 of their papers we have counts for
10 papers
Information-Theoretic Generalization Bounds for Sequential Decision Making
Futoshi Futami, Masahiro Fujisawa
Information-theoretic generalization bounds based on the supersample construction are a central tool for algorithm-dependent generalization analysis in the batch i.i.d.~setting. Ho…
-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
Masahiro Fujisawa, Futoshi Futami
Calibration of predicted probabilities is critical for reliable machine learning, yet it is poorly understood how standard training procedures yield well-calibrated models. This wo…
Information-theoretic Generalization Analysis for VQ-VAEs: A Role of Latent Variables
Futoshi Futami, Masahiro Fujisawa
Latent variables (LVs) play a crucial role in encoder-decoder models by enabling effective data compression, prediction, and generation. Although their theoretical properties, such…
Scalable Valuation of Human Feedback through Provably Robust Model Alignment
Masahiro Fujisawa, Masaki Adachi, Michael A. Osborne
Despite the importance of aligning language models with human preferences, crowd-sourced human feedback is often noisy -- for example, preferring less desirable responses -- posing…
PAC-Bayes Analysis for Recalibration in Classification
Masahiro Fujisawa, Futoshi Futami
Nonparametric estimation using uniform-width binning is a standard approach for evaluating the calibration performance of machine learning models. However, existing theoretical ana…
Information-theoretic Generalization Analysis for Expected Calibration Error
Futoshi Futami, Masahiro Fujisawa
While the expected calibration error (ECE), which employs binning, is widely adopted to evaluate the calibration performance of machine learning models, theoretical understanding o…
Time-Independent Information-Theoretic Generalization Bounds for SGLD
Futoshi Futami, Masahiro Fujisawa
We provide novel information-theoretic generalization bounds for stochastic gradient Langevin dynamics (SGLD) under the assumptions of smoothness and dissipativity, which are widel…
Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka +1
In this paper, we explores the expressivity and trainability of the Fourier Neural Operator (FNO). We establish a mean-field theory for the FNO, analyzing the behavior of the rando…
-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Masahiro Fujisawa, Takeshi Teshima, Issei Sato +1
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data dis…
Multilevel Monte Carlo Variational Inference
Masahiro Fujisawa, Issei Sato
We propose a variance reduction framework for variational inference using the Multilevel Monte Carlo (MLMC) method. Our framework is built on reparameterized gradient estimators an…