44 citations · 177 across the 24 of their papers we have counts for
7 papers · 2 filters
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès +2
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work w…
Calibrated Multiple-Output Quantile Regression with Representation Learning
Shai Feldman, Stephen Bates, Yaniv Romano
We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we…
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Anastasios N. Angelopoulos, Stephen Bates
Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failu…
Test-time Collective Prediction
Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates +1
An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to b…
Improving Conditional Coverage via Orthogonal Quantile Regression
Shai Feldman, Stephen Bates, Yaniv Romano
We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage. A typica…
Private Prediction Sets
Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic +1
In real-world settings involving consequential decision-making, the deployment of machine learning systems generally requires both reliable uncertainty quantification and protectio…