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Probabilistic Modeling

2591 directly classified papers

Papers per year

Papers

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks NIPS 2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes NIPS 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent NIPS 2020
A Unified Statistically Efficient Estimation Framework for Unnormalized Models AISTATS 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization NIPS 2020
Quantile Propagation for Wasserstein-Approximate Gaussian Processes NIPS 2020
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters NIPS 2020
Estimation of Skill Distribution from a Tournament NIPS 2020
Projected Stein Variational Gradient Descent NIPS 2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts NIPS 2020
Adaptive Sampling for Stochastic Risk-Averse Learning NIPS 2020
Improving Local Identifiability in Probabilistic Box Embeddings NIPS 2020
Riemannian Continuous Normalizing Flows NIPS 2020
Kernel Conditional Density Operators AISTATS 2020
Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020
Generalized probabilistic principal component analysis of correlated data JMLR 2020
Conditional Gaussian Distribution Learning for Open Set Recognition CVPR 2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA NIPS 2020
Do sequence-to-sequence VAEs learn global features of sentences? EMNLP 2020
Modeling Shared responses in Neuroimaging Studies through MultiView ICA NIPS 2020
Deep Bayesian Network for Visual Question Generation WACV 2020
Stochastic Deep Gaussian Processes over Graphs NIPS 2020
A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes AAAI 2020
PIDS: An Intelligent Electric Power Management Platform AAAI 2020
Learning Weighted Model Integration Distributions AAAI 2020