565 citations · 767 across the 14 of their papers we have counts for
6 papers · 1 filter
Risk-Controlling Model Selection via Guided Bayesian Optimization
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is t…
Efficiently Controlling Multiple Risks with Pareto Testing
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hype…
Calibrated Selective Classification
Adam Fisch, Tommi Jaakkola, Regina Barzilay
Selective classification allows models to abstain from making predictions (e.g., say "I don't know") when in doubt in order to obtain better effective accuracy. While typical selec…
Conformal Prediction Sets with Limited False Positives
Adam Fisch, Tal Schuster, Tommi Jaakkola +1
We develop a new approach to multi-label conformal prediction in which we aim to output a precise set of promising prediction candidates with a bounded number of incorrect answers.…
Few-shot Conformal Prediction with Auxiliary Tasks
Adam Fisch, Tal Schuster, Tommi Jaakkola +1
We develop a novel approach to conformal prediction when the target task has limited data available for training. Conformal prediction identifies a small set of promising output ca…
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
Adam Fisch, Tal Schuster, Tommi Jaakkola +1
In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction candidates -- in place of a single prediction. T…