Probabilistic Modeling

Offline Transition Modeling via Contrastive Energy Learning ICML 2024 On cyclical MCMC sampling AISTATS 2024 One Step Closer to Unbiased Aleatoric Uncertainty Estimation AAAI 2024 On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design AAAI 2024 On Hardware-efficient Inference in Probabilistic Circuits UAI 2024 On the Expressive Power of Tree-Structured Probabilistic Circuits NeurIPS 2024 On the Hardness of Probabilistic Neurosymbolic Learning ICML 2024 On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function NeurIPS 2024 On the Independence Assumption in Neurosymbolic Learning ICML 2024 On the Quantification of Image Reconstruction Uncertainty Without Training Data WACV 2024 On the True Distribution Approximation of Minimum Bayes-Risk Decoding NAACL 2024 OntoFact: Unveiling Fantastic Fact-Skeleton of LLMs via Ontology-Driven Reinforcement Learning AAAI 2024 Optimal score estimation via empirical Bayes smoothing COLT 2024 Parallel-in-Time Probabilistic Numerical ODE Solvers JMLR 2024 Parsing neural dynamics with infinite recurrent switching linear dynamical systems ICLR 2024 Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness RSS 2024 Particle Semi-Implicit Variational Inference NeurIPS 2024 PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness CVPR 2024 Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning NeurIPS 2024 PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX JMLR 2024 pgmpy: A Python Toolkit for Bayesian Networks JMLR 2024 PhyloGFN: Phylogenetic inference with generative flow networks ICLR 2024 Physics and Lie symmetry informed Gaussian processes ICML 2024 Physics-constrained learning of PDE systems with uncertainty quantified port-Hamiltonian models L4DC 2024 Physics-Informed Variational State-Space Gaussian Processes NeurIPS 2024