Bayesian & Probabilistic

Robust learning of tractable probabilistic models UAI 2022 Robust Neural Posterior Estimation and Statistical Model Criticism NeurIPS 2022 Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation ICLR 2022 Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods COLT 2022 Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent NeurIPS 2022 Sampling with Mirrored Stein Operators ICLR 2022 Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space NeurIPS 2022 Scalable Deep Gaussian Markov Random Fields for General Graphs ICML 2022 Scalable Gaussian-process regression and variable selection using Vecchia approximations JMLR 2022 Scale Mixtures of Neural Network Gaussian Processes ICLR 2022 Score-Based Diffusion meets Annealed Importance Sampling NeurIPS 2022 Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance NeurIPS 2022 Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems ICML 2022 Segmentation-Consistent Probabilistic Lesion Counting MIDL 2022 Self-Supervised Inference in State-Space Models ICLR 2022 Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions AAAI 2022 Semantic Probabilistic Layers for Neuro-Symbolic Learning NeurIPS 2022 Semi-Discrete Normalizing Flows through Differentiable Tessellation NeurIPS 2022 Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations IJCAI 2022 Sensing Cox Processes via Posterior Sampling and Positive Bases AISTATS 2022 Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games ICML 2022 SIXO: Smoothing Inference with Twisted Objectives NeurIPS 2022 Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk ACL 2022 Sliced Wasserstein variational inference ACML 2022 SMT-based weighted model integration with structure awareness UAI 2022