389 citations · 2.3k across the 33 of their papers we have counts for
10 papers · 1 filter
Machine Teaching: A New Paradigm for Building Machine Learning Systems
Patrice Y. Simard, Saleema Amershi, David M. Chickering +8
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning…
A Characterization of Prediction Errors
Christopher Meek
Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors an…
Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
David Maxwell Chickering, Christopher Meek
We introduce Selective Greedy Equivalence Search (SGES), a restricted version of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of GES but, unlike GES, ha…
Regularized Minimax Conditional Entropy for Crowdsourcing
Dengyong Zhou, Qiang Liu, John C. Platt +2
There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels…
Models and Selection Criteria for Regression and Classification
David Heckerman, Christopher Meek
When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatoryor input va…
Asymptotic Model Selection for Directed Networks with Hidden Variables
Dan Geiger, David Heckerman, Christopher Meek
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be u…