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Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian p...
Sparse Factor Analysis for Learning and Content Analytics
factor analysis sparse probit regression sparse logistic regression Bayesian latent factor analysis personalized learning
2013/4/28
We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain, and content analytics, which estimate the...
Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning
Asynchronous impulsive noise cyclostationary noise PLC OFDM sparse Bayesian learning
2013/4/27
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noi...
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness
Learning AMP Chain Graphs some Marginal Models Thereof under Faithfulness
2013/4/27
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability ...
Bayesian learning of joint distributions of objects
Bayesian learning joint distributions objects
2013/4/27
There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions,...
We consider a basic problem in unsupervised learning: learning an unknown \emph{Poisson Binomial Distribution} over $\{0,1,...,n\}$. A Poisson Binomial Distribution (PBD) is a sum $X = X_1 + ... + X_n...
Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions
Learning Weighted Trace-norm Arbitrary Sampling Distributions
2011/7/7
We provide rigorous guarantees on learning with the weighted trace-norm under arbitrary sampling distributions.