Bayesian & Probabilistic

Using Autodiff to Estimate Posterior Moments, Marginals and Samples UAI 2024 Value-Based Abstraction Functions for Abstraction Sampling UAI 2024 Variational Delayed Policy Optimization NeurIPS 2024 Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks JMLR 2024 Variational Gaussian Process Diffusion Processes AISTATS 2024 Variational Hybrid-Attention Framework for Multi-Label Few-Shot Aspect Category Detection AAAI 2024 Variational Resampling AISTATS 2024 Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes JMLR 2024 Walking the Values in Bayesian Inverse Reinforcement Learning UAI 2024 Wasserstein Proximal Coordinate Gradient Algorithms JMLR 2024 What does guidance do? A fine-grained analysis in a simple setting NeurIPS 2024 Zero Inflation as a Missing Data Problem: a Proxy-based Approach UAI 2024 Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion NeurIPS 2024 Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time ICLR 2023 A Corrected Expected Improvement Acquisition Function Under Noisy Observations ACML 2023 Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee ACML 2023 Actually Sparse Variational Gaussian Processes AISTATS 2023 Adaptive Annealed Importance Sampling with Constant Rate Progress ICML 2023 Adaptive Cholesky Gaussian Processes AISTATS 2023 Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification AISTATS 2023 Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models AAAI 2023 Adaptive Riemannian stochastic gradient descent and reparameterization for Gaussian mixture model fitting ACML 2023 Advancing Bayesian Optimization via Learning Correlated Latent Space NeurIPS 2023 A Dynamical System View of Langevin-Based Non-Convex Sampling NeurIPS 2023 A Finite-Particle Convergence Rate for Stein Variational Gradient Descent NeurIPS 2023