kullback-leibler divergence

Tree-structured Gaussian Process Approximations NeurIPS 2014 Approximation properties of DBNs with binary hidden units and real-valued visible units ICML 2013 Gaussian Kullback-Leibler Approximate Inference JMLR 2013 Integrated Non-Factorized Variational Inference NeurIPS 2013 Thompson Sampling for 1-Dimensional Exponential Family Bandits NeurIPS 2013 Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift ACML 2013 Variational Planning for Graph-based MDPs NeurIPS 2013 Affine Independent Variational Inference NeurIPS 2012 A Note on Metric Properties for Some Divergence Measures: The Gaussian Case ACML 2012 Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing NeurIPS 2012 Learning from the Wisdom of Crowds by Minimax Entropy NeurIPS 2012 Nonconvex Penalization Using Laplace Exponents and Concave Conjugates NeurIPS 2012 Robustness and risk-sensitivity in Markov decision processes NeurIPS 2012 Tight Bounds on Profile Redundancy and Distinguishability NeurIPS 2012 A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences COLT 2011 Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models AISTATS 2011 Expressive Power and Approximation Errors of Restricted Boltzmann Machines NeurIPS 2011 Semi-Supervised Learning with Measure Propagation JMLR 2011 Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification NeurIPS 2009 Estimation of Information Theoretic Measures for Continuous Random Variables NeurIPS 2008 Extended Grassmann Kernels for Subspace-Based Learning NeurIPS 2008 Better than least squares: comparison of objective functions for estimating linear-nonlinear models NeurIPS 2007 A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests JMLR 2006 Lower Bounds and Aggregation in Density Estimation JMLR 2006 PAC-learnability of Probabilistic Deterministic Finite State Automata JMLR 2004