Neural Information Processing Systems http://nips.cc/

AIDE: Fastand Communication Efficient Distributed Optimization.arXive-prints,

Pd i=1(1 li) (see (8)), whereli = x>i (X>X) 1xi isthei-thleveragescoreofX. Here, weshow(3).

We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth[8], by reducing the problem to achieving typical switching regret.

We will add more discussion on this in the next version of our paper, as suggested by the reviewer. For the full16 information setting, as far as we know there is no existing lower bound. Weare not aware of any25 existing results for tracking asmall set ofexperts with stochastic losses.

Associatedwitheach segment is a hypothesis from some hypothesis class. We give algorithms that are designed to exploit the scenario where there are many such segments but significantly fewer associated hypotheses.

We introduce a novel online multitask setting.

Forthis, wefirstconsiderthe -trickon(18), inwhichwesetw(t)= w+(t) w (t) where log w+(t)= rwL(w(t)), log w (t)=+ rwL(w(t)).