Variational Inference

Probabilistic Inference in Reinforcement Learning Done Right NeurIPS 2023 Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation NeurIPS 2023 Reparameterized Policy Learning for Multimodal Trajectory Optimization ICML 2023 Revisiting Structured Variational Autoencoders ICML 2023 RevUp: Revise and Update Information Bottleneck for Event Representation EACL 2023 Robust and Scalable Gaussian Process Regression and Its Applications CVPR 2023 Score-Based Diffusion Models as Principled Priors for Inverse Imaging ICCV 2023 Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks AAAI 2023 Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes ICML 2023 Taming Continuous Posteriors for Latent Variational Dialogue Policies AAAI 2023 The Bayesian Learning Rule JMLR 2023 Training Energy-Based Normalizing Flow with Score-Matching Objectives NeurIPS 2023 Undirected Probabilistic Model for Tensor Decomposition NeurIPS 2023 Variational Boosted Soft Trees AISTATS 2023 Variational Gaussian processes for linear inverse problems NeurIPS 2023 Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing NeurIPS 2023 Variational Inference with Gaussian Score Matching NeurIPS 2023 Variational Open-Domain Question Answering ICML 2023 Variational Wasserstein Barycenters with C-cyclical Monotonicity Regularization AAAI 2023 VaRT: Variational Regression Trees NeurIPS 2023 VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning NeurIPS 2023 Accurate Quantization of Measures via Interacting Particle-based Optimization ICML 2022 A Continuous Time Framework for Discrete Denoising Models NeurIPS 2022 Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model NeurIPS 2022 Amortized Inference for Causal Structure Learning NeurIPS 2022