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Bayesian & Probabilistic

3,963 papers (2 classified directly here)

Subtopics

Probabilistic Modeling (2,252) Variational Inference (802) Gaussian Processes (570) Bayesian Learning (455) Bayesian Optimization (221) Markov Chain Monte Carlo (143) Nonparametric Bayesian (86) Bayesian Networks (81) Conditional Random Fields (5)

Papers per year

9
118
124
394
151
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'25

Papers (including subtopics)

On the Estimation of Gaussian Mixture Copula Models ICML 2023
Context-Specific Causal Discovery for Categorical Data Using Staged Trees AISTATS 2023
Compositional Score Modeling for Simulation-Based Inference ICML 2023
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo NIPS 2023
Continuous Mixtures of Tractable Probabilistic Models AAAI 2023
Sequential Monte Carlo Learning for Time Series Structure Discovery ICML 2023
Attention as Implicit Structural Inference NIPS 2023
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions NIPS 2023
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition AISTATS 2023
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study EMNLP 2023
Bayesian Structure Scores for Probabilistic Circuits AISTATS 2023
NeuPSL: Neural Probabilistic Soft Logic IJCAI 2023
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models NIPS 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation AISTATS 2023
Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning NIPS 2023
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration NIPS 2023
Contextual Gaussian Process Bandits with Neural Networks NIPS 2023
Trusted Fine-Grained Image Classification through Hierarchical Evidence Fusion AAAI 2023
Learning Mixtures of Gaussians with Censored Data ICML 2023
Compositional Probabilistic and Causal Inference using Tractable Circuit Models AISTATS 2023
Approximate Stein Classes for Truncated Density Estimation ICML 2023
The Lauritzen-Chen Likelihood For Graphical Models AISTATS 2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates ICML 2023
Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks AAAI 2023
Variational Gaussian processes for linear inverse problems NIPS 2023