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JMLR
2012
12 years 2 days ago
Beyond Logarithmic Bounds in Online Learning
We prove logarithmic regret bounds that depend on the loss L∗ T of the competitor rather than on the number T of time steps. In the general online convex optimization setting, o...
Francesco Orabona, Nicolò Cesa-Bianchi, Cla...
ICASSP
2009
IEEE
14 years 4 months ago
Map approach to learning sparse Gaussian Markov networks
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Narges Bani Asadi, Irina Rish, Katya Scheinberg, D...
WEBI
2010
Springer
13 years 7 months ago
Expertise Matching via Constraint-Based Optimization
Abstract--Expertise matching, aiming to find the alignment between experts and queries, is a common problem in many real applications such as conference paper-reviewer assignment, ...
Wenbin Tang, Jie Tang, Chenhao Tan
TSP
2010
13 years 4 months ago
Code design for radar STAP via optimization theory
Abstract--In this paper, we deal with the problem of constrained code optimization for radar space-time adaptive processing (STAP) in the presence of colored Gaussian disturbance. ...
Antonio De Maio, Silvio De Nicola, Yongwei Huang, ...
FOCM
2002
97views more  FOCM 2002»
13 years 9 months ago
On the Riemannian Geometry Defined by Self-Concordant Barriers and Interior-Point Methods
We consider the Riemannian geometry defined on a convex set by the Hessian of a selfconcordant barrier function, and its associated geodesic curves. These provide guidance for the...
Yu. E. Nesterov, Michael J. Todd