We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known ...
We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest.
A primal-dual framework for the design and analysis of online strongly convex optimization algorithms is described and a new algorithm is derived that ...
Oct 12, 2021 · Abstract: We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms.
Dec 8, 2008 · We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the ...
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We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known ...
[PDF] Logarithmic Regret Algorithms for Online Convex Optimization
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In this paper, we give algorithms that achieve regret O(log(T)) for an arbitrary sequence of strictly convex functions (with bounded first and second ...
Missing: Mind Duality Gap:
This paper describes a family of prediction algorithms for strongly convex repeated games that attain logarithmic regret and applies it for solving ...
that the proposed online algorithm has a (cumulative) regret of O(. √T), where T ... Mind the duality gap: Logarithmic regret algorithms for online optimization.
Nov 9, 2015 · Mind the duality gap: Logarithmic regret algorithms for online optimization. In NIPS, 2008. Sham M. Kakade, Shai Shalev-Shwartz, and Ambuj ...