conftrace_
← Bayesian & Probabilistic
Artificial Intelligence › Bayesian & Probabilistic ›

Probabilistic Modeling

2,599 papers

Papers per year

1
8
78
87
232
84
'00
'05
'10
'15
'20
'25

Papers

A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification NIPS 2016
Inference by Reparameterization in Neural Population Codes NIPS 2016
Bayesian Optimization with Robust Bayesian Neural Networks NIPS 2016
Kronecker Determinantal Point Processes NIPS 2016
Pairwise Choice Markov Chains NIPS 2016
DISCO Nets : DISsimilarity COefficients Networks NIPS 2016
A Probabilistic Framework for Deep Learning NIPS 2016
New Liftable Classes for First-Order Probabilistic Inference NIPS 2016
Operator Variational Inference NIPS 2016
Constraints Based Convex Belief Propagation NIPS 2016
Select-and-Sample for Spike-and-Slab Sparse Coding NIPS 2016
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables NIPS 2016
Parameter Learning for Log-supermodular Distributions NIPS 2016
Gaussian Processes for Survival Analysis NIPS 2016
Mixed vine copulas as joint models of spike counts and local field potentials NIPS 2016
Proper Inner Product with Mean Displacement for Gaussian Noise Invariant ICA ACML 2016
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models AISTATS 2016
Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables AISTATS 2016
Survey Propagation beyond Constraint Satisfaction Problems AISTATS 2016
Latent Point Process Allocation AISTATS 2016
Universal Models of Multivariate Temporal Point Processes AISTATS 2016
Probabilistic Approximate Least-Squares AISTATS 2016
Approximate Inference Using DC Programming For Collective Graphical Models AISTATS 2016
Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016
A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models AISTATS 2016