3 citations · 4 across the 17 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…