惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

腾讯CDC
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog
S
SegmentFault 最新的问题
WordPress大学
WordPress大学
P
Proofpoint News Feed
Hugging Face - Blog
Hugging Face - Blog
MyScale Blog
MyScale Blog
A
About on SuperTechFans
雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
T
The Blog of Author Tim Ferriss
MongoDB | Blog
MongoDB | Blog
博客园 - 【当耐特】
The Cloudflare Blog
F
Fortinet All Blogs
小众软件
小众软件
博客园 - 三生石上(FineUI控件)
宝玉的分享
宝玉的分享
罗磊的独立博客
量子位
有赞技术团队
有赞技术团队
V
V2EX
Engineering at Meta
Engineering at Meta

cs.LG updates on arXiv.org

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Proper Calibeating
[Submitted on 26 May 2026 (v1), last revised 12 Sep 2026 (this v · 2026-05-26 · via cs.LG updates on arXiv.org

View PDF HTML (experimental)

Abstract:The classic concept of "calibrated forecasts" and its more recent refinement, "calibeating," are defined with respect to the standard quadratic scoring rule. We extend these notions to the class of proper scoring rules (for which the true distribution is an optimal forecast) and define \textit{proper calibration} and \textit{proper calibeating} by requiring the corresponding guarantees to hold uniformly over all bounded proper scoring rules. We first establish that calibration always implies proper calibration, whereas calibeating need not imply proper calibeating. Second, we show how to guarantee proper calibeating and proper multicalibeating; in particular, \textit{complete calibeating}---a strong form of calibeating that calibeats the joint binning---is always proper. Finally, we consider \textit{decision-making under uncertainty}, where one best replies to the forecasts. We establish that (proper) calibration is equivalent to universal no regret, and (proper) complete calibeating is equivalent to universal no regret together with universal subsuming of the reference sequence, where \textit{universal} refers to all bounded utility functions.

Submission history

From: Sergiu Hart [view email]
[v1] Tue, 26 May 2026 08:44:45 UTC (46 KB)
[v2] Fri, 5 Jun 2026 22:02:25 UTC (47 KB)
[v3] Sat, 12 Sep 2026 09:20:45 UTC (54 KB)