Probabilistic Modeling

Foundation Inference Models for Markov Jump Processes NeurIPS 2024 Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data ICLR 2024 Functional Directed Acyclic Graphs JMLR 2024 Functional Gradient Flows for Constrained Sampling NeurIPS 2024 Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling COLT 2024 Gaussian Processes on Cellular Complexes ICML 2024 Gaussian Process Neural Additive Models AAAI 2024 Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons AAAI 2024 Generative Conditional Distributions by Neural (Entropic) Optimal Transport ICML 2024 Generative Flow Networks as Entropy-Regularized RL AISTATS 2024 Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data JMLR 2024 Graphical Multioutput Gaussian Process with Attention ICLR 2024 Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification ICML 2024 Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models NeurIPS 2024 Hodge-Compositional Edge Gaussian Processes AISTATS 2024 How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression UAI 2024 Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty ICLR 2024 Identifying and Addressing Disparities in Public Libraries with Bayesian Latent Variable Modeling AAAI 2024 Image Background Serves as Good Proxy for Out-of-distribution Data ICLR 2024 Implicit Gaussian process representation of vector fields over arbitrary latent manifolds ICLR 2024 Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials ICML 2024 Improved Probabilistic Image-Text Representations ICLR 2024 Incremental Randomized Smoothing Certification ICLR 2024 Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation AAAI 2024 Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization UAI 2024