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<h2>Keywords</h2>

<h3>Methodologies and Learning Paradigms</h3>
<li>active learning
<li>approximate inference
<li>asymptotics, consistency
<li>Bayesian methods
<li>Bayesian nonparametrics
<li>boosting
<li>causality
<li>clustering
<li>computational learning theory
<li>deep learning
<li>discrete optimization
<li>ensemble methods
<li>feature selection
<li>game theory and mechanism design
<li>Gaussian processes
<li>graphical models
<li>information theory
<li>kernel methods	
<li>large margin methods
<li>large scale learning
<li>matrix and tensor factorization
<li>model selection
<li>Monte Carlo methods
<li>nonlinear embedding and manifold learning
<li>online learning
<li>optimization
<li>reinforcement learning and control
<li>semi-supervised learning
<li>sparse estimation, compressed sensing
<li>spectral methods
<li>statistical learning theory
<li>structured prediction

<h3>Application Areas</h3>
<li>computational biology and genomics
<li>computational neuroscience, cognitive modelling
<li>computer vision
<li>information systems, web processing
<li>robotics, human-computer interaction
<li>scientific data analysis
<li>social and information networks
<li>speech

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