3 citations · 5 across the 15 of their papers we have counts for
17 papers
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…
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…
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…
V-ECE: Estimating General Expected Calibration Errors
Eugène Berta, Sacha Braun, David Holzmüller +2
In probabilistic classification, calibration error (CE) measures the average divergence of predicted probabilities from , the true class distribution for…
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…
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…
Structured Matrix Scaling for Multi-Class Calibration
Eugène Berta, David Holzmüller, Michael I. Jordan +1
Post-hoc recalibration methods are widely used to ensure that classifiers provide faithful probability estimates. We argue that parametric recalibration functions based on logistic…
Adaptive Coverage Policies in Conformal Prediction
Etienne Gauthier, Francis Bach, Michael I. Jordan
Traditional conformal prediction methods construct prediction sets such that the true label falls within the set with a user-specified coverage level. However, poorly chosen covera…
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…
Backward Conformal Prediction
Etienne Gauthier, Francis Bach, Michael I. Jordan
We introduce , a method that guarantees conformal coverage while providing flexible control over the size of prediction sets. Unlike standar…
Minimum Volume Conformal Sets for Multivariate Regression
Sacha Braun, Liviu Aolaritei, Michael I. Jordan +1
Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. While much of the focus has been on univariate response variables…
E-Values Expand the Scope of Conformal Prediction
Etienne Gauthier, Francis Bach, Michael I. Jordan
Conformal prediction is a powerful framework for distribution-free uncertainty quantification. The standard approach to conformal prediction relies on comparing the ranks of predic…
Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores
Jivat Neet Kaur, Michael I. Jordan, Ahmed Alaa
Standard conformal prediction offers a marginal guarantee on coverage, but for prediction sets to be truly useful, they should ideally ensure coverage conditional on each test poin…
Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations
Liviu Aolaritei, Zheyu Oliver Wang, Julie Zhu +2
Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a signi…
Statistical Collusion by Collectives on Learning Platforms
Etienne Gauthier, Francis Bach, Michael I. Jordan
As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by co…
Rethinking Early Stopping: Refine, Then Calibrate
Eugène Berta, David Holzmüller, Michael I. Jordan +1
Machine learning classifiers often produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains. The quality of these predi…
Classifier Calibration with ROC-Regularized Isotonic Regression
Eugene Berta, Francis Bach, Michael Jordan
Calibration of machine learning classifiers is necessary to obtain reliable and interpretable predictions, bridging the gap between model confidence and actual probabilities. One p…