collaborators

16 papers

cs.LG2026

CalArena: A Large-Scale Post-Hoc Calibration Benchmark

Eugène Berta, David Holzmüller, Francis Bach +1

Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibration provides a simple and wi…

stat.ML2026

Conditional Coverage Diagnostics for Conformal Prediction

Sacha Braun, David Holzmüller, Michael I. Jordan +1

Evaluating conditional coverage remains one of the most persistent challenges in assessing the reliability of predictive systems. Although conformal methods can give guarantees on…

cs.GT2026

Anytime Detection of Strategic Deviations in Multi-Agent Systems

Etienne Gauthier, Francis Bach, Michael I. Jordan

In many multi-agent systems, agents interact repeatedly and are expected to settle into stable, rational behavior over time. Yet in practice, behavior often drifts, and detecting s…

stat.ML2026

Super-Level-Set Regression: Conditional Quantiles via Volume Minimization

Sacha Braun, Michael I. Jordan, Francis Bach

Constructing minimum-volume prediction regions that satisfy conditional coverage is a fundamental challenge in multivariate regression. Standard approaches rely on explicitly estim…

stat.ML2026

Multivariate Standardized Residuals for Conformal Prediction

Sacha Braun, Eugène Berta, Michael I. Jordan +1

While split conformal prediction guarantees marginal coverage, approaching the stronger property of conditional coverage is essential for reliable uncertainty quantification. Naive…

cs.LG2026

Explaining and Preventing Alignment Collapse in Iterative RLHF

Etienne Gauthier, Francis Bach, Michael I. Jordan

Reinforcement learning from human feedback (RLHF) typically assumes a static or non-strategic reward model (RM). In iterative deployment, however, the policy generates the data on…