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Probabilistic Modeling

2,599 papers

Papers per year

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Papers

State-Space Inference and Learning with Gaussian Processes AISTATS 2010
Sequential Monte Carlo Samplers for Dirichlet Process Mixtures AISTATS 2010
Modeling annotator expertise: Learning when everybody knows a bit of something AISTATS 2010
Continuous Time Bayesian Network Reasoning and Learning Engine JMLR 2010
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation JMLR 2010
FastInf: An Efficient Approximate Inference Library JMLR 2010
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models JMLR 2010
Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010
Learning Non-Stationary Dynamic Bayesian Networks JMLR 2010
Probabilistic Lane Estimation using Basis Curves RSS 2010
Learning models of object structure NIPS 2009
Spatial Normalized Gamma Processes NIPS 2009
Rethinking LDA: Why Priors Matter NIPS 2009
Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning NIPS 2009
Gaussian process regression with Student-t likelihood NIPS 2009
A Stochastic approximation method for inference in probabilistic graphical models NIPS 2009
Modeling the spacing effect in sequential category learning NIPS 2009
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism NIPS 2009
Which graphical models are difficult to learn? NIPS 2009
Sparse and Locally Constant Gaussian Graphical Models NIPS 2009
Individuation, Identification and Object Discovery NIPS 2009
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process NIPS 2009
Bayesian Nonparametric Models on Decomposable Graphs NIPS 2009
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies NIPS 2009
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features NIPS 2009