34 citations · 43 across the 4 of their papers we have counts for
4 papers
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
Charles Marx, Sofian Zalouk, Stefano Ermon
Calibration ensures that probabilistic forecasts meaningfully capture uncertainty by requiring that predicted probabilities align with empirical frequencies. However, many existing…
Modular Conformal Calibration
Charles Marx, Shengjia Zhao, Willie Neiswanger +1
Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for recalibration, which use…
Predictive Multiplicity in Classification
Charles T. Marx, Flavio du Pin Calmon, Berk Ustun
Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicti…
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Charles T. Marx, Richard Lanas Phillips, Sorelle A. Friedler +2
Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can b…