Bayesian & Probabilistic

Gaussian Differential Privacy on Riemannian Manifolds NeurIPS 2023 Gaussian Processes with Errors in Variables: Theory and Computation JMLR 2023 Gaussian Process Probes (GPP) for Uncertainty-Aware Probing NeurIPS 2023 Generalizable Neural Fields as Partially Observed Neural Processes ICCV 2023 Generalization in the Face of Adaptivity: A Bayesian Perspective NeurIPS 2023 Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation NeurIPS 2023 Generalized Belief Transport NeurIPS 2023 Generative Augmented Flow Networks ICLR 2023 Generative Label Enhancement with Gaussian Mixture and Partial Ranking AAAI 2023 Geometric Neural Diffusion Processes NeurIPS 2023 GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies NeurIPS 2023 GFlowNet Foundations JMLR 2023 GFlowNets and variational inference ICLR 2023 Global Identifiability of ℓ_1-based Dictionary Learning via Matrix Volume Optimization NeurIPS 2023 Global Perception Based Autoregressive Neural Processes ICCV 2023 GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks ICLR 2023 GPEX, A Framework For Interpreting Artificial Neural Networks NeurIPS 2023 Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning CVPR 2023 Gradient Boosting Performs Gaussian Process Inference ICLR 2023 Gradient-Free Kernel Stein Discrepancy NeurIPS 2023 Graph Denoising Diffusion for Inverse Protein Folding NeurIPS 2023 Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution ICLR 2023 Guided Deep Kernel Learning UAI 2023 Hallucinated adversarial control for conservative offline policy evaluation UAI 2023 Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning NeurIPS 2023