NIPS
2007
13 years 6 months ago
2007
Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization ...
NIPS
2007
13 years 6 months ago
2007
We give a new class of outer bounds on the marginal polytope, and propose a cutting-plane algorithm for efficiently optimizing over these constraints. When combined with a concav...
NIPS
2007
13 years 6 months ago
2007
We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori k...
NIPS
2007
13 years 6 months ago
2007
The peak location in a population of phase-tuned neurons has been shown to be a more reliable estimator for disparity than the peak location in a population of position-tuned neur...
NIPS
2007
13 years 6 months ago
2007
Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these...
NIPS
2007
13 years 6 months ago
2007
We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
NIPS
2007
13 years 6 months ago
2007
We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an...
NIPS
2007
13 years 6 months ago
2007
We propose a method for reconstruction of human brain states directly from functional neuroimaging data. The method extends the traditional multivariate regression analysis of dis...
NIPS
2007
13 years 6 months ago
2007
We investigate a new, convex relaxation of an expectation-maximization (EM) variant that approximates a standard objective while eliminating local minima. First, a cautionary resu...
NIPS
2007
13 years 6 months ago
2007
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...