Mar 31, 2012 · In this paper we isolate a property of matrices, which we call (beta,tau)-decomposability, and derive an efficient online learning algorithm, ...

Our algorithm yields a nearly optimal regret bound of O(pnlog(n)T) for this problem. This is the first efficient algorithm that achieves near optimal regret. 2.

In this paper, we design the optimal algorithm, adversary and regret for the case of 3 experts. Further, we show that the optimal algorithm for 2 and 3 experts ...

In this paper we isolate a property of matrices, which we call (β, τ)-decomposability, and derive an efficient online learning algorithm that enjoys a regret ...

Crucially, the paper reframes the well-known problems of online max cut, learning a team ranking (“gambling”), and trace-norm regularized matrix com- pletion ( ...

Mar 31, 2012 · By analyzing the decomposability of cut matrices, triangular matrices, and low trace- norm matrices, we derive near optimal regret bounds for ...

By analyzing the decomposability of cut matrices, low trace-norm matrices, and triangular matrices, we derive near-optimal regret bounds for online max-cut, ...

By analyzing the decomposability of cut matrices, low trace-norm matrices, and triangular matrices, we derive near-optimal regret bounds for online max-cut, ...

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