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Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Signal Recovery Temporally Correlated Bayesian Learning
2011/3/22
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorith...
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs
Machine Learning (stat.ML) Neural and Evolutionary Computing (cs.NE)
2010/12/17
Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, because of the intracti...
Model Selection by Loss Rank for Classification and Unsupervised Learning
Classification graphical models loss rank principle model selection
2010/11/9
Hutter (2007) recently introduced the loss rank principle (LoRP) as a general-purpose principle for model selection. The LoRP enjoys many attractive prop-erties and deserves further investigations. Th...
No-Regret Reductions for Imitation Learning and Structured Prediction
No-Regret Reductions for Imitation Learning Structured Prediction
2010/11/9
Sequential prediction problems such as imitation learning, where future observations depend on
previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning.
Particle Learning and Smoothing
Mixture Kalman filter parameter learning particle learning sequential inference smoothing state filtering
2010/11/9
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models.Our approach extends existing particle methods by incorporating th...
Online Multiple Kernel Learning for Structured Prediction
Online Multiple Kernel Learning r Structured Prediction
2010/10/19
Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning...
Comment on “Fastest learning in small-world neural networks”
Feed-forward neural network small-world network random network
2010/3/11
This comment reexamines Simard et al.’s work in [D. Simard, L. Nadeau, H. Kröger, Phys.
Lett. A 336 (2005) 8-15]. We found that Simard et al. calculated mistakenly the local connectivity
length...
This paper constructs a statistical model of learning that suggests a systematic way of measuring the persistence of treatment effects in education. This method is straightforward to implement, allows...
Learning gradients on manifolds
classification feature selection manifold learning regression shrinkage estimator Tikhonov regularization
2010/3/10
A common belief in high-dimensional data analysis is that data are concentrated on a lowdimensional
manifold. This motivates simultaneous dimension reduction and regression on manifolds.
We provide ...
In query learning, the goal is to identify an unknown object while minimizing the number of \yes" or\no" questions (queries) posed about that object. A well-studied algorithm for query learning is kno...
Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
learning regression estimation logistic regression oracle inequalities sparsity prior Langevin Monte-Carlo
2010/3/18
We consider the problem of regression learning for deterministic design and independent random errors.We start by proving a sharp PAC-Bayesian type bound for the exponentially weighted aggregate (EWA)...
Efficient Bayesian Learning in Social Networks with Gaussian Estimators
Efficient Bayesian Learning Social Networks Gaussian Estimators
2010/3/10
We propose a simple and efficient Bayesian model of iterative learning on social networks.
This model is efficient in two senses: the process both results in an optimal belief, and can
be carried ou...
Learning to Trust E-Trailers: Strategies Used By Consumers in a Distrustful Environment
E-Trailers Consumers a Distrustful Environment
2010/1/15
This article draws on a phenomenological study of understanding six early adopters'
successful online shopping experiences. Narratives of their online purchasing experiences
suggest that learning t...
Regularization in kernel learning
Regression reproducing kernel Hilbert space regulation leastsquares model selection
2010/3/9
Under mild assumptions on the kernel, we obtain the best known
error rates in a regularized learning scenario taking place in the corresponding
reproducing kernel Hilbert space (RKHS). The main nove...
This paper investigates the problem of selection and estimation in a high dimensional
regression-type model. We propose a procedure with no optimization called LOL, for Learning
Out of Leaders. LOL ...