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Learning subgaussian classes : Upper and minimax bounds
Learning subgaussian classes Upper and minimax bounds
2013/6/14
We obtain sharp oracle inequalities for the empirical risk minimization procedure in the regression model under the assumption that the target $Y$ and the model $\cF$ are subgaussian. The bound we obt...
Spectral Methods for Learning Multivariate Latent Tree Structure
Spectral Methods Learning Multivariate Latent Tree Structure
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...
Bayesian and L1 Approaches to Sparse Unsupervised Learning
Bayesian L1 Approaches Sparse Unsupervised Learning
2011/7/6
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborat...
ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples
ProDiGe PRioritization Disease Genes multitask machine learning positive unlabeled examples
2011/7/6
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists o...
Bayesian multitask inverse reinforcement learning
Bayesian inference multitask learning inverse reinforce-ment learning
2011/7/6
We generalise the problem of inverse reinforcement learning to multiple tasks, from a set of demonstrations. Each demonstration may represent one expert trying to solve a different task.
State-Observation Sampling and the Econometrics of Learning Models
Hidden Markov model particle filter state-observation sampling learning indirect inference forecasting state space model value at risk
2011/6/20
In nonlinear state-space models, sequential learning about the hidden state can proceed
by particle filtering when the density of the observation conditional on the state is available
analytically (...
Optimal Reinforcement Learning for Gaussian Systems
Optimal Reinforcement Learning Gaussian Systems
2011/7/5
The exploration-exploitation tradeoff is among the central challenges of reinforcement learning. A hypothetical exact Bayesian learner would provide the optimal solution, but is intractable in general...