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Probabilistic Modeling

2591 directly classified papers

Papers per year

Papers

Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NIPS 2018
Algebraic tests of general Gaussian latent tree models NIPS 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression ICML 2018
AMR Parsing as Graph Prediction with Latent Alignment ACL 2018
Lifted Weighted Mini-Bucket NIPS 2018
Matrix-normal models for fMRI analysis AISTATS 2018
Bayesian Structure Learning for Dynamic Brain Connectivity AISTATS 2018
Implicit Quantile Networks for Distributional Reinforcement Learning ICML 2018
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity NIPS 2018
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation NIPS 2018
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language NIPS 2018
Differentially Private Regression with Gaussian Processes AISTATS 2018
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data AISTATS 2018
Probabilistic Machine Learning: Models, Algorithms and a Programming Library IJCAI 2018
Predictive Uncertainty Estimation via Prior Networks NIPS 2018
Distributionally Robust Graphical Models NIPS 2018
Model-based targeted dimensionality reduction for neuronal population data NIPS 2018
Direct Estimation of Differences in Causal Graphs NIPS 2018
Learning and Inference in Hilbert Space with Quantum Graphical Models NIPS 2018
Exponentiated Strongly Rayleigh Distributions NIPS 2018
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference AISTATS 2018
Structured Factored Inference for Probabilistic Programming AISTATS 2018
Optimality of Approximate Inference Algorithms on Stable Instances AISTATS 2018
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs AISTATS 2018
Learning Determinantal Point Processes in Sublinear Time AISTATS 2018