Bayesian & Probabilistic

A Kernel Perspective of Skip Connections in Convolutional Networks ICLR 2023 Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler COLT 2023 A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data AISTATS 2023 An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks JMLR 2023 A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference NeurIPS 2023 An Ordinal Latent Variable Model of Conflict Intensity ACL 2023 Any-scale Balanced Samplers for Discrete Space ICLR 2023 Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods AAAI 2023 Approximate Stein Classes for Truncated Density Estimation ICML 2023 A Probabilistic Framework for Discovering New Intents ACL 2023 A Probabilistic Framework for Lifelong Test-Time Adaptation CVPR 2023 Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? ICML 2023 A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods NeurIPS 2023 A Semi-parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks AAAI 2023 Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression NeurIPS 2023 A theory of continuous generative flow networks ICML 2023 A theory of representation learning gives a deep generalisation of kernel methods ICML 2023 Attention as Implicit Structural Inference NeurIPS 2023 Automatically marginalized MCMC in probabilistic programming ICML 2023 Batch Bayesian Optimization For Replicable Experimental Design NeurIPS 2023 Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors JMLR 2023 Bayesian Data Selection JMLR 2023 Bayesian Federated Learning: A Survey IJCAI 2023 Bayesian Federated Neural Matching That Completes Full Information AAAI 2023 Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning EMNLP 2023