196,959 research outputs found
Learning mixtures of structured distributions over discrete domains
Let be a class of probability distributions over the discrete
domain We show that if satisfies a rather
general condition -- essentially, that each distribution in can
be well-approximated by a variable-width histogram with few bins -- then there
is a highly efficient (both in terms of running time and sample complexity)
algorithm that can learn any mixture of unknown distributions from
We analyze several natural types of distributions over , including
log-concave, monotone hazard rate and unimodal distributions, and show that
they have the required structural property of being well-approximated by a
histogram with few bins. Applying our general algorithm, we obtain
near-optimally efficient algorithms for all these mixture learning problems.Comment: preliminary full version of soda'13 pape
Auto-Encoding Sequential Monte Carlo
We build on auto-encoding sequential Monte Carlo (AESMC): a method for model
and proposal learning based on maximizing the lower bound to the log marginal
likelihood in a broad family of structured probabilistic models. Our approach
relies on the efficiency of sequential Monte Carlo (SMC) for performing
inference in structured probabilistic models and the flexibility of deep neural
networks to model complex conditional probability distributions. We develop
additional theoretical insights and introduce a new training procedure which
improves both model and proposal learning. We demonstrate that our approach
provides a fast, easy-to-implement and scalable means for simultaneous model
learning and proposal adaptation in deep generative models
A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
The Tree Augmented Naive Bayes classifier is a type of probabilistic
graphical model that can represent some feature dependencies. In this work, we
propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes
(HRE-TAN) algorithm, which considers removing the hierarchical redundancy
during the classifier learning process, when coping with data containing
hierarchically structured features. The experiments showed that HRE-TAN obtains
significantly better predictive performance than the conventional Tree
Augmented Naive Bayes classifier, and enhanced the robustness against
imbalanced class distributions, in aging-related gene datasets with Gene
Ontology terms used as features.Comment: International Conference on Machine Learning (ICML 2016)
Computational Biology Worksho
Learning Tree Distributions by Hidden Markov Models
Hidden tree Markov models allow learning distributions for tree structured
data while being interpretable as nondeterministic automata. We provide a
concise summary of the main approaches in literature, focusing in particular on
the causality assumptions introduced by the choice of a specific tree visit
direction. We will then sketch a novel non-parametric generalization of the
bottom-up hidden tree Markov model with its interpretation as a
nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho
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