research
∙
05/12/2023
Surfacing Biases in Large Language Models using Contrastive Input Decoding
Ensuring that large language models (LMs) are fair, robust and useful re...
share
research
∙
11/28/2022
Malign Overfitting: Interpolation Can Provably Preclude Invariance
Learned classifiers should often possess certain invariance properties m...
research
∙
10/25/2022
Useful Confidence Measures: Beyond the Max Score
An important component in deploying machine learning (ML) in safety-crit...
research
∙
04/10/2022
Active Learning with Label Comparisons
Supervised learning typically relies on manual annotation of the true la...
research
∙
03/18/2022
Decision-Making under Miscalibration
ML-based predictions are used to inform consequential decisions about in...
research
∙
11/21/2021
On Fairness and Stability in Two-Sided Matchings
There are growing concerns that algorithms, which increasingly make or i...
research
∙
10/27/2021
Revisiting Sanity Checks for Saliency Maps
Saliency methods are a popular approach for model debugging and explaina...
research
∙
10/02/2021
Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification
In many machine learning settings there is an inherent tension between f...
research
∙
05/20/2021
Multi-group Agnostic PAC Learnability
An agnostic PAC learning algorithm finds a predictor that is competitive...
research
∙
11/26/2020
Outcome Indistinguishability
Prediction algorithms assign numbers to individuals that are popularly u...
research
∙
10/09/2019
Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data
A fancy learning algorithm A outperforms a baseline method B when they a...
research
∙
04/03/2019
Preference-Informed Fairness
As algorithms are increasingly used to make important decisions pertaini...
research
∙
03/08/2018