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Relevance As a Metric for Evaluating Machine Learning Algorithms
Machine learning algorithms performance metric proba-bilistic approach
2013/4/28
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the conce...
ABC Reinforcement Learning
ABC Reinforcement Learning
2013/4/28
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prio...
On Sparsity Inducing Regularization Methods for Machine Learning
Sparsity Inducing Regularization Methods for Machine Learning
2013/5/2
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer ...
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary. Dictionaries are either available a...
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
Online Learning Markov Decision Processes Adversarially Chosen Transition Probability Distributions
2013/5/2
We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed...
Learning Stable Multilevel Dictionaries for Sparse Representation of Images
Learning Stable Multilevel Dictionaries Sparse Representation Images
2013/4/28
Dictionaries adapted to the data provide superior performance when compared to predefined dictionaries in applications involving sparse representations. Algorithmic stability and generalization are de...
Second-Order Non-Stationary Online Learning for Regression
Second-Order Non-Stationary Online Learning for Regression
2013/4/28
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to h...
From Agreement to Asymptotic Learning
Agreement Asymptotic Learning Bayesian agents asymptotic learning communication model
2011/6/20
Since Aumann's Agreement Theorem [3], the study of the exchange of information between
Bayesian agents has resulted in broad theoretical insight into the phenomenon of agreement
and the dynamics tha...