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02/10/2022
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Image-to-image regression is an important learning task, used frequently...
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02/08/2022
Conformal prediction for the design problem
In many real-world deployments of machine learning, we use a prediction ...
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01/31/2022
Causal Inference with Orthogonalized Regression Adjustment: Taming the Phantom
Standard regression adjustment gives inconsistent estimates of causal ef...
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01/26/2022
Confidence Intervals for the Generalisation Error of Random Forests
Out-of-bag error is commonly used as an estimate of generalisation error...
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01/25/2022
Online Active Learning with Dynamic Marginal Gain Thresholding
The blessing of ubiquitous data also comes with a curse: the communicati...
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10/03/2021
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce Learn then Test, a framework for calibrating machine learni...
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10/02/2021
Calibrated Multiple-Output Quantile Regression with Representation Learning
We develop a method to generate predictive regions that cover a multivar...
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07/15/2021
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Black-box machine learning learning methods are now routinely used in hi...
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06/22/2021
Test-time Collective Prediction
An increasingly common setting in machine learning involves multiple par...
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06/01/2021
Improving Conditional Coverage via Orthogonal Quantile Regression
We develop a method to generate prediction intervals that have a user-sp...
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04/16/2021
Testing for Outliers with Conformal p-values
This paper studies the construction of p-values for nonparametric outlie...
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04/01/2021
Cross-validation: what does it estimate and how well does it do it?
Cross-validation is a widely-used technique to estimate prediction error...
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02/11/2021
Private Prediction Sets
In real-world settings involving consequential decision-making, the depl...
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01/07/2021
Distribution-Free, Risk-Controlling Prediction Sets
While improving prediction accuracy has been the focus of machine learni...
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09/29/2020
Uncertainty Sets for Image Classifiers using Conformal Prediction
Convolutional image classifiers can achieve high predictive accuracy, bu...
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06/08/2020
Achieving Equalized Odds by Resampling Sensitive Attributes
We present a flexible framework for learning predictive models that appr...
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02/22/2020
Causal Inference in Genetic Trio Studies
We introduce a method to rigorously draw causal inferences—inferences im...
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03/01/2019