approximate inference

Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings UAI 2026 Approximate Bayesian Inference via Bitstring Representations UAI 2025 Approximate Lifted Model Construction IJCAI 2025 Bayesian Score Calibration for Approximate Models JMLR 2025 DIME: Diffusion-Based Maximum Entropy Reinforcement Learning ICML 2025 Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI AISTATS 2025 KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks ICLR 2025 Abductive Reasoning in Logical Credal Networks NeurIPS 2024 Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo AISTATS 2024 An Adaptive Implicit Hitting Set Algorithm for MAP and MPE Inference PGM 2024 An Approximate Skolem Function Counter AAAI 2024 Approximate Control for Continuous-Time POMDPs AISTATS 2024 A Study of Bayesian Neural Network Surrogates for Bayesian Optimization ICLR 2024 Fearless Stochasticity in Expectation Propagation NeurIPS 2024 Low-rank Variational Bayes correction to the Laplace method JMLR 2024 On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models NeurIPS 2024 PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX JMLR 2024 Reparameterization invariance in approximate Bayesian inference NeurIPS 2024 Surrogate Bayesian Networks for Approximating Evolutionary Games AISTATS 2024 VISA: Variational Inference with Sequential Sample-Average Approximations NeurIPS 2024 A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference NeurIPS 2023 Approximate Inference in Logical Credal Networks IJCAI 2023 Approximate inference of marginals using the IBIA framework NeurIPS 2023 Approximate Thompson Sampling via Epistemic Neural Networks UAI 2023 A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-Run Langevin Flow for Approximate Inference AAAI 2023