← Back to all news
arXiv Machine Learning By Vasily Bokov (aQa, Leiden University, The Netherlands, LIACS, Leiden University, Leiden, The Netherlands, Honda Research Institute Europe GmbH, Offenbach, Germany), Sebastian Schmitt (Honda Research Institute Europe GmbH, Offenbach, Germany), Vedran Dunjko (aQa, Leiden University, The Netherlands, LIACS, Leiden University, Leiden, The Netherlands), Hao Wang (aQa, Leiden University, The Netherlands, LIACS, Leiden University, Leiden, The Netherlands)

Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach

Read the original on arXiv Machine Learning →

The paper introduces a PAC‑Bayesian, algorithm‑agnostic framework to quantify the value of privileged information (PI) in Learning Using Privileged Information (LUPI). By comparing the tightest achievable risk bounds with and without PI, the authors derive a training‑time metric that estimates the maximum potential gain from PI without requiring test data. Experiments in supervised and unsupervised settings show a strong correspondence between this metric and actual test‑time performance improvements.

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.

Related stories

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
safety
More like this →
arXiv Machine Learning
Sep 14

Explanations-Driven Active Feature Acquisition for Algorithmic Recourse

The paper introduces Explanation-Driven Feature Acquisition (EDFA), a method that jointly optimizes algorithmic recourse and feature acquisition by selecting features based on explanatory value per unit cost. Using Markov Blanket theory, EDFA unifies various explanation types and provides distribution‑free validity guarantees for recourse derived from partial information. Experiments on seven datasets show that EDFA requires fewer features than existing active feature acquisition baselines while maintaining accuracy and producing more actionable recourse.

By Vinura Galwaduge, Jagath Samarabandu
benchmarks
More like this →