arXiv Machine Learning By Ivane Antonov, Sohom Mukherjee, Richard Pibernik, Yo Joong Choe

Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters

Read the original on arXiv Machine Learning →

arXiv:2607. 11653v1 Announce Type: new Abstract: Black-box conditional quantile forecasts are widely used for sequential decisions under asymmetric costs, such as inventory planning in supply chain management.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Statistics ML
Sep 16

Statistical Inference for Score Decompositions

arXiv:2603.04275v2 Announce Type: replace-cross Abstract: We introduce inference methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable...

By Timo Dimitriadis, Marius Puke
arXiv AI
Sep 15

When Should a World Model Move? Loss-Conditioned State Execution

The paper introduces loss‑conditioned state execution, a model‑agnostic technique that decides whether to apply a world model’s proposed state change or keep the current state based on whether the change reduces downstream loss. It formalizes state movability as the existence of a loss‑reducing feasible correction and constructs loss‑specific proposals from predictive distributions, executing them only when a groupwise lower confidence bound on loss improvement is positive. Experiments on forecasting and dynamics benchmarks show that the method accepts updates for a subset of cases, achieving lower bounded loss than persistence or always executing the proposal, and highlights that event predictability and loss‑based decisions must be evaluated separately.

By Jintao Xu, Zhengyu Chen, Ben Zhang, Yongzhi Qi, Jianshen Zhang