33 citations · 42 across the 16 of their papers we have counts for
6 papers · 1 filter
ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty
Yuyang Dai, Yuting Tang, Futoshi Futami +1
Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional class probabilities unknown. To…
Robust and Computationally Efficient Linear Contextual Bandits under Adversarial Corruption and Heavy-Tailed Noise
Naoto Tani, Futoshi Futami
We study linear contextual bandits under adversarial corruption and heavy-tailed noise with finite -th moments for some . Existing work that addresses both adver…
-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…
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…