Bayesian Inference

Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation AAAI 2023 Distance-to-Set Priors and Constrained Bayesian Inference AISTATS 2023 Empirical Bayes via ERM and Rademacher complexities: the Poisson model COLT 2023 Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations ACL 2023 Estimation of Counterfactual Interventions under Uncertainties ACML 2023 Federated Learning with Uncertainty via Distilled Predictive Distributions ACML 2023 FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation AAAI 2023 Few-Shot Non-Line-of-Sight Imaging With Signal-Surface Collaborative Regularization CVPR 2023 Folded Hamiltonian Monte Carlo for Bayesian Generative Adversarial Networks ACML 2023 ForecastPFN: Synthetically-Trained Zero-Shot Forecasting NeurIPS 2023 Formal Verification of Bayesian Mechanisms AAAI 2023 Free Energy of Bayesian Convolutional Neural Network with Skip Connection ACML 2023 Generalization in the Face of Adaptivity: A Bayesian Perspective NeurIPS 2023 Generalized Belief Transport NeurIPS 2023 Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning CVPR 2023 Gradient Boosting Performs Gaussian Process Inference ICLR 2023 Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution ICLR 2023 Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography ICLR 2023 Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks AAAI 2023 Learning Analytical Posterior Probability for Human Mesh Recovery CVPR 2023 Learning in temporally structured environments ICLR 2023 Local Risk Bounds for Statistical Aggregation COLT 2023 Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples ICLR 2023 Minimum Description Length Control ICLR 2023 Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model AAAI 2023