conftrace_

Bayesian & Probabilistic

4,733 papers (2 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Probabilistic Modeling (2,617) Variational Inference (898) Gaussian Processes (666) Bayesian Learning (483) Markov Chain Monte Carlo (374) Bayesian Optimization (284) Nonparametric Bayesian (174) Bayesian Networks (149) Conditional Random Fields (39)

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Papers (including subtopics)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting ICLR 2024 Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data AISTATS 2024 Kermut: Composite kernel regression for protein variant effects NeurIPS 2024 Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization AAAI 2024 Kernel Thinning JMLR 2024 Knowledge Intensive Learning of Credal Networks UAI 2024 Label-wise Aleatoric and Epistemic Uncertainty Quantification UAI 2024 Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited ICLR 2024 Large-Scale Gaussian Processes via Alternating Projection AISTATS 2024 Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters AAAI 2024 Learning Diffusion Priors from Observations by Expectation Maximization NeurIPS 2024 Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains UAI 2024 Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG ICLR 2024 Learning multivariate temporal point processes via the time-change theorem AISTATS 2024 Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport JMLR 2024 Learning relevant contextual variables within Bayesian optimization UAI 2024 Learning Representations on the Unit Sphere: Investigating Angular Gaussian and Von Mises-Fisher Distributions for Online Continual Learning AAAI 2024 Learning Sparse Codes with Entropy-Based ELBOs AISTATS 2024 Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models AISTATS 2024 Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals NeurIPS 2024 Lifting Architectural Constraints of Injective Flows ICLR 2024 Limited-Supervised Multi-Label Learning with Dependency Noise AAAI 2024 Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024 Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability UAI 2024 Linear Opinion Pooling for Uncertainty Quantification on Graphs UAI 2024