Probabilistic Modeling

GFlowNet-EM for Learning Compositional Latent Variable Models ICML 2023 GFlowNet Foundations JMLR 2023 Gibbsian Polar Slice Sampling ICML 2023 GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks AAAI 2023 Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration NeurIPS 2023 HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model ACL 2023 Improved dimension dependence of a proximal algorithm for sampling COLT 2023 Improved Discretization Analysis for Underdamped Langevin Monte Carlo COLT 2023 Improving Adaptive Conformal Prediction Using Self-Supervised Learning AISTATS 2023 Input uncertainty propagation through trained neural networks ICML 2023 Integrating Random Effects in Deep Neural Networks JMLR 2023 Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions NeurIPS 2023 Interpreting Sentiment Composition with Latent Semantic Tree ACL 2023 Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network NeurIPS 2023 Knowledge Graph Embedding by Normalizing Flows AAAI 2023 Knowledge Graph Embeddings using Neural Ito Process: From Multiple Walks to Stochastic Trajectories ACL 2023 Learning and Testing Latent-Tree Ising Models Efficiently COLT 2023 Learning-augmented count-min sketches via Bayesian nonparametrics JMLR 2023 Learning GFlowNets From Partial Episodes For Improved Convergence And Stability ICML 2023 Learning Hidden Markov Models Using Conditional Samples COLT 2023 Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma AAAI 2023 Learning Mixtures of Gaussians with Censored Data ICML 2023 Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task EMNLP 2023 Lifted Inference with Linear Order Axiom AAAI 2023 Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data ICML 2023