1.4k citations · 2.5k across the 47 of their papers we have counts for
5 papers · 1 filter
Omnipredictors
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold +2
Loss minimization is a dominant paradigm in machine learning, where a predictor is trained to minimize some loss function that depends on an uncertain event (e.g., "will it rain to…
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms
Myra Cheng, Maria De-Arteaga, Lester Mackey +1
Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However…
Programming Puzzles
Tal Schuster, Ashwin Kalyan, Oleksandr Polozov +1
We introduce a new type of programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis, and release an open-source dataset o…
Towards optimally abstaining from prediction with OOD test examples
Adam Tauman Kalai, Varun Kanade
A common challenge across all areas of machine learning is that training data is not distributed like test data, due to natural shifts, "blind spots," or adversarial examples; such…
Efficient Learning with Arbitrary Covariate Shift
Adam Kalai, Varun Kanade
We give an efficient algorithm for learning a binary function in a given class C of bounded VC dimension, with training data distributed according to P and test data according to Q…