Probabilistic Modeling

Probabilistic Reasoning and Learning for Trustworthy AI AAAI 2023 Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions EMNLP 2023 Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models ICML 2023 Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing ICML 2023 Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group AISTATS 2023 Regularized Joint Mixture Models JMLR 2023 Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling COLT 2023 Retrospective Uncertainties for Deep Models using Vine Copulas AISTATS 2023 Revisiting Structured Variational Autoencoders ICML 2023 Sampling-Based Accuracy Testing of Posterior Estimators for General Inference ICML 2023 Sampling with Mollified Interaction Energy Descent ICLR 2023 Scale-Invariant Infinite Hierarchical Topic Model ACL 2023 ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images CVPR 2023 Score-based Data Assimilation NeurIPS 2023 Semiparametric Inference Using Fractional Posteriors JMLR 2023 Semi-supervised Community Detection via Structural Similarity Metrics ICLR 2023 Separate but Equal: Equality in Belief Propagation for Single Cycle Graphs AAAI 2023 Single Image Depth Prediction Made Better: A Multivariate Gaussian Take CVPR 2023 SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals AISTATS 2023 Sparse Markov Models for High-dimensional Inference JMLR 2023 Sparse Maximum Margin Learning from Multimodal Human Behavioral Patterns AAAI 2023 Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck AISTATS 2023 Statistical Efficiency of Score Matching: The View from Isoperimetry ICLR 2023 Statistical Inference for Noisy Incomplete Binary Matrix JMLR 2023 Stimulus Verification Is a Universal and Effective Sampler in Multi-Modal Human Trajectory Prediction CVPR 2023