87 citations · 170 across the 16 of their papers we have counts for
4 papers · 1 filter
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Continual Learning: Applications and the Road Forward
Eli Verwimp, Rahaf Aljundi, Shai Ben-David +17
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting wh…
Impossibility of Characterizing Distribution Learning -- a simple solution to a long-standing problem
Tosca Lechner, Shai Ben-David
We consider the long-standing question of finding a parameter of a class of probability distributions that characterizes its PAC learnability. We provide a rather surprising answer…
On Computable Online Learning
Niki Hasrati, Shai Ben-David
We initiate a study of computable online (c-online) learning, which we analyze under varying requirements for "optimality" in terms of the mistake bound. Our main contribution is t…