Probabilistic Modeling

Piecewise Linear Transformation – Propagating Aleatoric Uncertainty in Neural Networks AAAI 2024 Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models ICLR 2024 Polynomial Semantics of Tractable Probabilistic Circuits UAI 2024 Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024 Position: Understanding LLMs Requires More Than Statistical Generalization ICML 2024 Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice NeurIPS 2024 Preferential Normalizing Flows NeurIPS 2024 Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts EMNLP 2024 Principled Bayesian Optimization in Collaboration with Human Experts NeurIPS 2024 Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness NeurIPS 2024 Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics NeurIPS 2024 Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization JMLR 2024 Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes ICML 2024 Probabilistic Generating Circuits - Demystified ICML 2024 Probabilistic Integral Circuits AISTATS 2024 Probabilistic Modeling for Sequences of Sets in Continuous-Time AISTATS 2024 Probabilistic Modeling of Interpersonal Coordination Processes ICML 2024 Probabilistic Neural Circuits AAAI 2024 Probabilistic Offline Policy Ranking with Approximate Bayesian Computation AAAI 2024 Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization ICLR 2024 Products, Abstractions and Inclusions of Causal Spaces UAI 2024 Protein Discovery with Discrete Walk-Jump Sampling ICLR 2024 Proving Test Set Contamination in Black-Box Language Models ICLR 2024 Qualitative Mechanism Independence NeurIPS 2024 Quasi-Bayes meets Vines NeurIPS 2024