55 citations · 70 across the 5 of their papers we have counts for
5 papers
Reinforcement Ranking
Hengshuai Yao, Dale Schuurmans
We introduce a new framework for web page ranking -- reinforcement ranking -- that improves the stability and accuracy of Page Rank while eliminating the need for computing the sta…
Learning Bayesian Nets that Perform Well
Russell Greiner, Adam J. Grove, Dale Schuurmans
A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by…
Monte Carlo Inference via Greedy Importance Sampling
Dale Schuurmans, Finnegan Southey
We present a new method for conducting Monte Carlo inference in graphical models which combines explicit search with generalized importance sampling. The idea is to reduce the vari…
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…
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…