In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothe...
Variable selection is an important and practical problem that arises in analysis of many high-dimensional datasets. Convex optimization procedures that arise from relaxing the NP-...
We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in a...
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its s...
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshu...
This paper proposes a novel Bayesian approximation inference method for mixture modeling. Our key idea is to factorize marginal log-likelihood using a variational distribution ove...
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Models such as pairwise conditional random fields (CRFs) are extremely popular in computer vision and various other machine learning disciplines. However, they have limited expre...
This document contains supplementary material to the article ‘Statistical test for consistent estimation of causal effects in linear non-Gaussian models’, AISTATS 2012. A tabl...
In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations...
John Cunningham, Zoubin Ghahramani, Carl Edward Ra...
This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, ...
David Buchman, Mark W. Schmidt, Shakir Mohamed, Da...