430 citations · 995 across the 30 of their papers we have counts for
30 papers
STUDY: Socially Aware Temporally Causal Decoder Recommender Systems
Eltayeb Ahmed, Diana Mincu, Lauren Harrell +2
Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to…
Participatory Personalization in Classification
Hailey Joren, Chirag Nagpal, Katherine Heller +1
Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do…
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo +14
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantl…
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
Ghassen Jerfel, Serena Wang, Clara Fannjiang +3
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler diver…
Deep Cox Mixtures for Survival Regression
Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh +1
Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example…
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…