
Analysis of a Design Pattern for Teaching with Features and Labels
We study the task of teaching a machine to classify objects using featur...
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Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
We introduce Selective Greedy Equivalence Search (SGES), a restricted ve...
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Regularized Minimax Conditional Entropy for Crowdsourcing
There is a rapidly increasing interest in crowdsourcing for data labelin...
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Structure and Parameter Learning for Causal Independence and Causal Interaction Models
This paper discusses causal independence models and a generalization of ...
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Quantifier Elimination for Statistical Problems
Recent improvement on Tarski's procedure for quantifier elimination in t...
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Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (2003)
This is the Proceedings of the Nineteenth Conference on Uncertainty in A...
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Dependency Networks for Collaborative Filtering and Data Visualization
We describe a graphical model for probabilistic relationshipsan alter...
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Perfect TreeLike Markovian Distributions
We show that if a strictly positive joint probability distribution for a...
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Using Temporal Data for Making Recommendations
We treat collaborative filtering as a univariate time series estimation ...
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CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
We describe CFW, a computationally efficient algorithm for collaborative...
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Factorization of Discrete Probability Distributions
We formulate necessary and sufficient conditions for an arbitrary discre...
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Finding Optimal Bayesian Networks
In this paper, we derive optimality results for greedy Bayesiannetwork ...
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Inference for Multiplicative Models
The paper introduces a generalization for known probabilistic models suc...
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Asymptotic Model Selection for Directed Networks with Hidden Variables
We extend the Bayesian Information Criterion (BIC), an asymptotic approx...
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Models and Selection Criteria for Regression and Classification
When performing regression or classification, we are interested in the c...
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A Bayesian Approach to Learning Bayesian Networks with Local Structure
Recently several researchers have investigated techniques for using data...
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Learning Mixtures of DAG Models
We describe computationally efficient methods for learning mixtures in w...
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Graphical Models and Exponential Families
We provide a classification of graphical models according to their repre...
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Staged Mixture Modelling and Boosting
In this paper, we introduce and evaluate a datadriven staged mixture mo...
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Practically Perfect
The property of perfectness plays an important role in the theory of Bay...
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LargeSample Learning of Bayesian Networks is NPHard
In this paper, we provide new complexity results for algorithms that lea...
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Machine Teaching: A New Paradigm for Building Machine Learning Systems
The current processes for building machine learning systems require prac...
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