Alexandre Perez-Lebel — accepted AI-conference papers

2026

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald, Marine Le Morvan, Gael Varoquaux +1

ICML 2026poster

Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ o…

Cited by 1 Source ↗Scholar ↗

2025

Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration

Alexandre Perez-Lebel, Gael Varoquaux, Sanmi Koyejo, Matthieu Doutreligne, Marine Le Morvan

AISTATS 2025poster

Probabilistic classifiers are central for making informed decisions under uncertainty. Based on the maximum expected utility principle, optimal decision rules can be derived using the posterior class probabilities and misclassification costs. Yet, in practice only learned approximations of the oracl…

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2025

To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers

Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau, Antoine Saillenfest, Thomas Bonald

ICML 2025poster

Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings frequently relies on heuristic decision rules, which may not align with task-specific evaluation metrics. In this work, we p…

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2024

Reconfidencing LLMs from the Grouping Loss Perspective

Lihu Chen, Alexandre Perez-Lebel, Fabian M. Suchanek, Gaël Varoquaux

EMNLP 2024finding

Large Language Models (LLMs), such as GPT and LLaMA, are susceptible to generating hallucinated answers in a confident tone. While previous efforts to elicit and calibrate confidence scores have shown some success, they often overlook biases towards certain groups, such as specific nationalities. Ex…

Cited by 8 Source ↗PDF ↗Scholar ↗

2023

Beyond calibration: estimating the grouping loss of modern neural networks

Alexandre Perez-Lebel, Marine Le Morvan, Gael Varoquaux

ICLR 2023poster

The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier…

Cited by 37 Source ↗PDF ↗code ↗Scholar ↗