Image de couverture de livre

Reassessing How to Compare and Improve the Calibration of Machine Learning Models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chidambaram, Muthu, Ge, Rong
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Statistics Theory
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

On the Limitations of Temperature Scaling for Distributions with Overlaps
par: Chidambaram, Muthu, et autres
Publié: (2023)

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
par: Chidambaram, Muthu, et autres
Publié: (2024)

Valid Inference for Machine Learning Model Parameters
par: Dey, Neil, et autres
Publié: (2023)

Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning
par: Patil, Pratik, et autres
Publié: (2024)

On Uncertainty Calibration for Equivariant Functions
par: Berman, Edward, et autres
Publié: (2025)

Scoring Rules and Calibration for Imprecise Probabilities
par: Fröhlich, Christian, et autres
Publié: (2024)

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
par: Arratia, Argimiro, et autres
Publié: (2025)

Information-theoretic Generalization Analysis for Expected Calibration Error
par: Futami, Futoshi, et autres
Publié: (2024)

Orthogonal Causal Calibration
par: Whitehouse, Justin, et autres
Publié: (2024)

Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees
par: Fan, Jianqing, et autres
Publié: (2025)

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
par: Ji, Wenlong, et autres
Publié: (2025)

Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling
par: Li, Yufan, et autres
Publié: (2025)

Review and Prospect of Algebraic Research in Equivalent Framework between Statistical Mechanics and Machine Learning Theory
par: Watanabe, Sumio
Publié: (2024)

Comparing Comparators in Generalization Bounds
par: Hellström, Fredrik, et autres
Publié: (2023)

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
par: Guo, Zijian, et autres
Publié: (2022)

Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics
par: Martin-Maroto, Fernando, et autres
Publié: (2026)

Concentration and Calibration in Predictive Bayesian Inference
par: Frazier, David T., et autres
Publié: (2026)

Improved Convergence of Score-Based Diffusion Models via Prediction-Correction
par: Pedrotti, Francesco, et autres
Publié: (2023)

$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
par: Fujisawa, Masahiro, et autres
Publié: (2025)

Asymptotic Classification Error for Heavy-Tailed Renewal Processes
par: Rong, Xinhui, et autres
Publié: (2024)

Batched Nonparametric Contextual Bandits
par: Jiang, Rong, et autres
Publié: (2024)

The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
par: Jiang, Rong, et autres
Publié: (2025)

RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
par: Hao, Sai, et autres
Publié: (2026)

Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
par: Landa, Boris, et autres
Publié: (2024)

Small Ensemble-based Data Assimilation: A Machine Learning-Enhanced Data Assimilation Method with Limited Ensemble Size
par: Li, Zhilin, et autres
Publié: (2025)

Design Experiments to Compare Multi-armed Bandit Algorithms
par: Meng, Huiling, et autres
Publié: (2026)

Treatment Effect Estimation with Observational Network Data using Machine Learning
par: Emmenegger, Corinne, et autres
Publié: (2022)

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
par: van der Laan, Lars, et autres
Publié: (2025)

Geometric Calibration and Neutral Zones for Uncertainty-Aware Multi-Class Classification
par: Das, Soumojit, et autres
Publié: (2025)

Improved Algorithm and Bounds for Successive Projection
par: Jin, Jiashun, et autres
Publié: (2024)

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
par: Chidambaram, Muthu, et autres
Publié: (2022)

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
par: Byambadalai, Undral, et autres
Publié: (2024)

How well behaved is finite dimensional Diffusion Maps?
par: Bo, Wenyu, et autres
Publié: (2024)

Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
par: Cheng, Ziheng, et autres
Publié: (2025)

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
par: Letteri, Ivan
Publié: (2025)

Principled Out-of-Distribution Generalization via Simplicity
par: Ge, Jiawei, et autres
Publié: (2025)

Learning Survival Models with Right-Censored Reporting Delays
par: Shikuri, Yuta, et autres
Publié: (2025)

Higher-Order Causal Structure Learning with Additive Models
par: Enouen, James, et autres
Publié: (2025)

Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands
par: Csáji, Balázs Csanád, et autres
Publié: (2024)

Improved Guarantees for Langevin Monte Carlo with Average Smoothness
par: Dalalyan, Arnak S., et autres
Publié: (2026)