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Bayesian & Probabilistic

4,335 papers (2 classified directly here)

Subtopics

Probabilistic Modeling (2,599) Bayesian Learning (1,663) Bayesian Inference (523) Variational Inference (107) Gaussian Processes (6) Bayesian Networks (5)

Papers per year

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Papers (including subtopics)

Particle Gibbs for Infinite Hidden Markov Models NIPS 2015
The Population Posterior and Bayesian Modeling on Streams NIPS 2015
Fast Lifted MAP Inference via Partitioning NIPS 2015
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number NIPS 2015
MCMC for Variationally Sparse Gaussian Processes NIPS 2015
A Gaussian Process Model of Quasar Spectral Energy Distributions NIPS 2015
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings NIPS 2015
Learning structured densities via infinite dimensional exponential families NIPS 2015
Learning Structured Output Representation using Deep Conditional Generative Models NIPS 2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families NIPS 2015
Deep Temporal Sigmoid Belief Networks for Sequence Modeling NIPS 2015
Segregated Graphs and Marginals of Chain Graph Models NIPS 2015
Expectation Particle Belief Propagation NIPS 2015
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression NIPS 2015
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators NIPS 2015
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring NIPS 2015
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees NIPS 2015
Tractable Learning for Complex Probability Queries NIPS 2015
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models NIPS 2015
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) NIPS 2015
Deep Poisson Factor Modeling NIPS 2015
Bounding the Cost of Search-Based Lifted Inference NIPS 2015
Copula variational inference NIPS 2015
Softstar: Heuristic-Guided Probabilistic Inference NIPS 2015
Learning to Linearize Under Uncertainty NIPS 2015