Papers
572 papers found
An Approximate Analytical Approach to Resampling Averages
Dörthe Malzahn, Manfred Opper
Assessing Approximate Inference for Binary Gaussian Process Classification
Malte Kuss, Carl Edward Rasmussen
A Unifying View of Sparse Approximate Gaussian Process Regression
Joaquin Quiñonero-Candela, Carl Edward Rasmussen
Expectation Consistent Approximate Inference
Manfred Opper, Ole Winther
Loop Corrections for Approximate Inference on Factor Graphs
Joris M. Mooij, Hilbert J. Kappen
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther, Manfred Opper
Learning Approximate Sequential Patterns for Classification
Zeeshan Syed, Piotr Indyk, John Guttag
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
Jie Chen, Haw-ren Fang, Yousef Saad
Approximate Tree Kernels
Konrad Rieck, Tammo Krueger, Ulf Brefeld et al.
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation
Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov
FastInf: An Efficient Approximate Inference Library
Ariel Jaimovich, Ofer Meshi, Ian McGraw et al.
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
Antti Honkela, Tapani Raiko, Mikael Kuusela et al.
Approximate Marginals in Latent Gaussian Models
Botond Cseke, Tom Heskes
Robust Approximate Bilinear Programming for Value Function Approximation
Marek Petrik, Shlomo Zilberstein
Gaussian Kullback-Leibler Approximate Inference
Edward Challis, David Barber
Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
Manfred Opper, Ulrich Paquet, Ole Winther
Fast SVM Training Using Approximate Extreme Points
Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi
Approximate Modified Policy Iteration and its Application to the Game of Tetris
Bruno Scherrer, Mohammad Ghavamzadeh, Victor Gabillon et al.
Dual Control for Approximate Bayesian Reinforcement Learning
Edgar D. Klenske, Philipp Hennig
Approximate Newton Methods for Policy Search in Markov Decision Processes
Thomas Furmston, Guy Lever, David Barber
Stochastic Gradient Descent as Approximate Bayesian Inference
Stephan Mandt, Matthew D. Hoffman, David M. Blei