most citedSecond-Order Non-Stationary Online Learning for Regression

21 citations · 32 across the 5 of their papers we have counts for

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cs.LG20132 cited

Advice-Efficient Prediction with Expert Advice

Yevgeny Seldin, Peter Bartlett, Koby Crammer

Advice-efficient prediction with expert advice (in analogy to label-efficient prediction) is a variant of prediction with expert advice game, where on each round of the game we are…

cs.LG20137 cited

A Last-Step Regression Algorithm for Non-Stationary Online Learning

Edward Moroshko, Koby Crammer

The goal of a learner in standard online learning is to maintain an average loss close to the loss of the best-performing single function in some class. In many real-world problems…

cs.LG201321 cited

Second-Order Non-Stationary Online Learning for Regression

Nina Vaits, Edward Moroshko, Koby Crammer

The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algori…

cs.LG2013

Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic Regret

Edward Moroshko, Koby Crammer

In online learning the performance of an algorithm is typically compared to the performance of a fixed function from some class, with a quantity called regret. Forster proposed a l…

cs.LG20122 cited

More Is Better: Large Scale Partially-supervised Sentiment Classification - Appendix

Yoav Haimovitch, Koby Crammer, Shie Mannor

We describe a bootstrapping algorithm to learn from partially labeled data, and the results of an empirical study for using it to improve performance of sentiment classification us…