We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
Online prediction methods are typically presented as serial algorithms running on a single processor. However, in the age of web-scale prediction problems, it is increasingly comm...
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin X...
Abstract. Users of online dating sites are facing information overload that requires them to manually construct queries and browse huge amount of matching user profiles. This beco...
Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular, with over a million...
The analysis of online least squares estimation is at the heart of many stochastic sequential decision-making problems. We employ tools from the self-normalized processes to provi...