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Probabilistic Modeling

2591 directly classified papers

Papers per year

Papers

Learning spatiotemporal trajectories from manifold-valued longitudinal data NIPS 2015
Swept Approximate Message Passing for Sparse Estimation ICML 2015
Reified Context Models ICML 2015
Inference for determinantal point processes without spectral knowledge NIPS 2015
Message Passing for Collective Graphical Models ICML 2015
Vector-Space Markov Random Fields via Exponential Families ICML 2015
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods ICML 2015
Landmarking Manifolds with Gaussian Processes ICML 2015
On the Relationship between Sum-Product Networks and Bayesian Networks ICML 2015
Sampling from Probabilistic Submodular Models NIPS 2015
IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation RSS 2015
A New Perspective and Extension of the Gaussian Filter RSS 2015
Lifted Inference Rules With Constraints NIPS 2015
Active Learning for Structured Probabilistic Models With Histogram Approximation CVPR 2015
Picture: A Probabilistic Programming Language for Scene Perception CVPR 2015
Learning to Linearize Under Uncertainty NIPS 2015
Learning Structured Output Representation using Deep Conditional Generative Models NIPS 2015
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces NIPS 2015
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks JMLR 2015
A Gaussian Process Model of Quasar Spectral Energy Distributions NIPS 2015
From MAP to Marginals: Variational Inference in Bayesian Submodular Models NIPS 2014
Restricted Boltzmann machines modeling human choice NIPS 2014
Clustered factor analysis of multineuronal spike data NIPS 2014
Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014
Latent Gaussian Models for Topic Modeling AISTATS 2014