4.3k citations · 6.9k across the 51 of their papers we have counts for
6 papers · 1 filter
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Shaojun Wang, Dale Schuurmans, Fuchun Peng +1
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is differ…
Monte Carlo Matrix Inversion Policy Evaluation
Fletcher Lu, Dale Schuurmans
In 1950, Forsythe and Leibler (1950) introduced a statistical technique for finding the inverse of a matrix by characterizing the elements of the matrix inverse as expected values…
Maximum Margin Bayesian Networks
Yuhong Guo, Dana Wilkinson, Dale Schuurmans
We consider the problem of learning Bayesian network classifiers that maximize the marginover a set of classification variables. We find that this problem is harder for Bayesian ne…
Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering
Yuhong Guo, Dale Schuurmans
We present a new approach to learning the structure and parameters of a Bayesian network based on regularized estimation in an exponential family representation. Here we show that,…
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
Yaoliang Yu, James Neufeld, Ryan Kiros +2
We demonstrate that almost all non-parametric dimensionality reduction methods can be expressed by a simple procedure: regularized loss minimization plus singular value truncation.…
Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering
Yao-Liang Yu, Dale Schuurmans
When data is sampled from an unknown subspace, principal component analysis (PCA) provides an effective way to estimate the subspace and hence reduce the dimension of the data. At…