21 citations · 28 across the 3 of their papers we have counts for
3 papers
cs.LG2013★ 7 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.LG2013★ 21 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…