Abstract not found

Abstract—This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal matching pursuit and thresholding algorithms are analyzed under both adversarial and Gaussian noise models

the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques

, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel

We present a simple sparse greedy technique to approximate the maximum a posteriori estimate of Gaussian Processes with much improved scaling behaviour in the sample size m. In particular, computational requirements are O(n m), storage is O(nm), the cost for prediction is O(n) and the cost

in the lit-erature. However, the existing approaches are unable to handle ultra-high dimensional datasets and there is a crucial need to de-velop methods that are both highly scal-able and memory-efficient. In this paper, we present GINCO, a blocked greedy method for sparse inverse covariance matrix

Spectral dimensionality reduction methods and spectral clustering methods require computation of the principal eigenvectors of an n × n matrix where n is the number of examples. Following up on previously proposed techniques to speed-up kernel methods by focusing on a subset of m examples, we study

Greedy importance sampling is an unbiased estimation technique that re-duces the variance of standard importance sampling by explicitly search-ing for modes in the estimation objective. Previous work has demon-strated the feasibility of implementing this method and proved that the technique

Abstract I present a simple variation of importance sampling that explicitly search-es for important regions in the target distribution. I prove that the technique yields unbiased estimates, and show empirically it can reduce thevariance of standard Monte Carlo estimators. This is achieved

Techniques for robot monitoring and diagnosis have been developed that perform state estimation using probabilistic hybrid discrete/continuous models. Exact inference in hybrid dynamic systems is, in general, intractable. Approximate algorithms are based on either 1) greedy search, as in the case