arXiv Statistics ML
Subdata Selection for Measurement-Constrained Linear Regression under Model Misspecification
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
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.
arXiv Machine Learning
Oct 21
Dissecting the Impact of Model Misspecification in Data-driven Optimization
arXiv:2503.00626v3 Announce Type: replace Abstract: Data-driven optimization aims to translate a machine learning model into decision-making by optimizing decisions on estimated costs. Such a pipelin...
arXiv Machine Learning
Sep 22
Sparse Regression Distilled from a Single Robust Fit
arXiv:2609.23937v1 Announce Type: cross Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillati...
arXiv Machine Learning
Oct 7
Assumption-lean logistic regression with missing covariates
arXiv:2610.07292v1 Announce Type: cross Abstract: Missing covariates are frequently encountered in supervised learning problems, and classical methods for estimation using such data use carefully cho...
arXiv Machine Learning
Jul 22
How Benchmark Prediction from Fewer Data Misses the Mark
arXiv:2506. 07673v2 Announce Type: replace Abstract: Large language model (LLM) evaluation is increasingly costly, prompting interest in methods that speed up evaluation by shrinking benchmark datasets.
arXiv Machine Learning
3d ago
Pro-Active Inference
The paper introduces Pro-Active Inference (PAI), a method for efficiently estimating a population mean when only a subset of outcomes can be observed. PAI proactively selects which observation to label next from a covariate pool, updating machine‑learning predictions online, and provides finite‑sample unbiasedness, asymptotic validity, and an improved variance estimator. Experiments on eight datasets show that PAI outperforms Sequential Active Inference in most settings, while diagnostics reveal when additional adaptivity is beneficial or detrimental.