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ICML
2010
IEEE
13 years 11 months ago
Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process
We propose a novel dependent hierarchical Pitman-Yor process model for discrete data. An incremental Monte Carlo inference procedure for this model is developed. We show that infe...
Nicholas Bartlett, David Pfau, Frank Wood
ISSTA
2004
ACM
14 years 3 months ago
Optimal strategies for testing nondeterministic systems
This paper deals with testing of nondeterministic software systems. We assume that a model of the nondeterministic system is given by a directed graph with two kind of vertices: s...
Lev Nachmanson, Margus Veanes, Wolfram Schulte, Ni...
ICML
2009
IEEE
14 years 11 months ago
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
Linli Xu, Martha White, Dale Schuurmans
ICSEA
2009
IEEE
13 years 8 months ago
Integrating Formal Methods with Model-Driven Engineering
In this paper, we present our position and experience on integrating formal methods with the Model-driven Engineering (MDE) approach to software development. Both these two approa...
Angelo Gargantini, Elvinia Riccobene, Patrizia Sca...
ICML
2000
IEEE
14 years 11 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...