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Probabilistic Modeling

2591 directly classified papers

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Papers

Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes NIPS 2019
Embarrassingly Parallel Inference for Gaussian Processes JMLR 2019
Universal Boosting Variational Inference NIPS 2019
No-Regret Learning in Unknown Games with Correlated Payoffs NIPS 2019
On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019
Generic Inference in Latent Gaussian Process Models JMLR 2019
Poisson-Randomized Gamma Dynamical Systems NIPS 2019
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model NIPS 2019
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling JMLR 2019
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization JMLR 2019
Fast and Flexible Inference of Joint Distributions from their Marginals ICML 2019
Learning Models from Data with Measurement Error: Tackling Underreporting ICML 2019
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap ICML 2019
Stein Point Markov Chain Monte Carlo ICML 2019
Bayesian Hierarchical Dynamic Model for Human Action Recognition CVPR 2019
Robust Variational Bayesian Point Set Registration ICCV 2019
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control NIPS 2019
On Tractable Computation of Expected Predictions NIPS 2019
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses NIPS 2019
Amortized Bethe Free Energy Minimization for Learning MRFs NIPS 2019
Flexible Modeling of Diversity with Strongly Log-Concave Distributions NIPS 2019
Band-Limited Gaussian Processes: The Sinc Kernel NIPS 2019
Tensor Variable Elimination for Plated Factor Graphs ICML 2019
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms NIPS 2019
Expressive power of tensor-network factorizations for probabilistic modeling NIPS 2019