搜索结果: 1-15 共查到“理论统计学 Learning”相关记录55条 . 查询时间(0.449 秒)
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...
Learning the Structure of Mixed Graphical Models
Learning the Structure Mixed Graphical Models
2015/8/21
We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and di...
This paper addresses the problem of unsupervised feature learning for text data.Our method is grounded in the principle of minimum description length and uses a dictionary-based compression scheme to ...
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Distributed Optimization Statistical Learning via Alternating Direction Method Multipliers
2015/7/9
Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasi...
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Hierarchically-coupled hidden Markov models learning kinetic rates single-molecule data
2013/6/14
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measure...
Learning Policies for Contextual Submodular Prediction
Learning Policies Contextual Submodular Prediction
2013/6/14
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using subm...
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
Generalization Ability Online Learning Algorithms Pairwise Loss Functions
2013/6/14
In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample...
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...
Regret Bounds for Reinforcement Learning with Policy Advice
Regret Bounds Reinforcement LearningPolicy Advice
2013/6/13
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with p...
Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee
Mixtures of Bernoulli Templates Two-Round EM Performance Guarantee
2013/6/13
Dasgupta showed that a two-round variant of the EM algorithm can learn mixture of Gaussian distributions with near optimal precision with high probability if the Gaussian distributions are well separa...
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
Efficient Reinforcement Learning High Dimensional Linear Quadratic Systems
2013/4/28
We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control ...
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...
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Distributed Learning Gaussian Graphical Models Marginal Likelihoods
2013/4/28
We consider distributed estimation of the inverse covariance matrix, also called the concentration matrix, in Gaussian graphical models. Traditional centralized estimation often requires iterative and...
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
Reinforcement Learning Uncertain Knowledge Probabilistic Reasoning Optimal Behavior in Polynomial Time
2013/5/2
Bayesian Reinforcement Learning (RL) is capable of not only incorporating domain knowledge, but also solving the exploration-exploitation dilemma in a natural way. As Bayesian RL is intractable except...
Monte-Carlo utility estimates for Bayesian reinforcement learning
Monte-Carlo estimates Bayesian reinforcement learning
2013/5/2
This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct ...