In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint.

In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint.

We study the infinite-horizon restless bandit problem with the average reward criterion, in both discrete-time and continuous-time settings.

With Drop-DTW,we address temporal step localization on instructional videos, representation learning from noisy videos, and cross-modal representation learning for audio-visual retrievaland localization.

Weproveupper and lower bounds on the amount ofqueries that are needed for an auditor to succeed within this framework.