Probabilistic Modeling

Integrating Uncertainty Awareness into Conformalized Quantile Regression AISTATS 2024 Interpreting Predictive Probabilities: Model Confidence or Human Label Variation? EACL 2024 Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective ICML 2024 Joint learning of Gaussian graphical models in heterogeneous dependencies of high-dimensional transcriptomic data ACML 2024 Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data AISTATS 2024 Kernelised Normalising Flows ICLR 2024 LanguageFlow: Advancing Diffusion Language Generation with Probabilistic Flows NAACL 2024 Latent Gaussian Graphical Models with Golazo Penalty PGM 2024 Latent variable model for high-dimensional point process with structured missingness ICML 2024 Learning-Augmented Dynamic Submodular Maximization NeurIPS 2024 Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport JMLR 2024 Learning Staged Trees from Incomplete Data PGM 2024 Learning to Continually Learn with the Bayesian Principle ICML 2024 Learning to Embed Distributions via Maximum Kernel Entropy NeurIPS 2024 Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models AISTATS 2024 Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference ICML 2024 Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments ICLR 2024 Lifted Causal Inference in Relational Domains CLeaR 2024 Linear Opinion Pooling for Uncertainty Quantification on Graphs UAI 2024 Linear Uncertainty Quantification of Graphical Model Inference NeurIPS 2024 Liouville Flow Importance Sampler ICML 2024 LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language NeurIPS 2024 LP-based Construction of DC Decompositions for Efficient Inference of Markov Random Fields AISTATS 2024 MALIBO: Meta-learning for Likelihood-free Bayesian Optimization ICML 2024 Markov Constraint as Large Language Model Surrogate IJCAI 2024