arXiv Machine Learning
Jul 7

Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.

By Diego Marcondes, Cl\'audia Peixoto
arXiv Machine Learning
Jul 27

Smart predict-then-robustly-optimize

arXiv:2607. 21773v1 Announce Type: new Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance in the covariate feature space.

By Aakil Caunhye, Xuefei Lu, Belen Martin-Barragan
arXiv Machine Learning
Oct 12

Fast Rates for Nonstationary Weighted Risk Minimization

The paper investigates weighted empirical risk minimization under nonstationary data, decomposing the excess risk into a learning component and a drift component. It establishes oracle inequalities for the learning error under mixing conditions, uniformly over arbitrary weight classes, and derives explicit bounds for drift errors in square loss settings, covering structural‑break and smoothly drifting processes. The analysis is extended to auto‑regression problems with linear models, basis approximations, and neural networks, revealing that optimal weights balance learning and drift via a bias–variance trade‑off and recover minimax‑optimal rates in unweighted, stationary cases.

By Tobias Brock, Thomas Nagler