Bayesian Inference

Fortuna: A Library for Uncertainty Quantification in Deep Learning JMLR 2024 FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning NeurIPS 2024 Full Bayesian Significance Testing for Neural Networks AAAI 2024 Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting IJCAI 2024 Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data ICLR 2024 Fundamental Limits of Non-Linear Low-Rank Matrix Estimation COLT 2024 Gated Inference Network: Inference and Learning State-Space Models NeurIPS 2024 Gaussian Mixture Models with Rare Events JMLR 2024 Gaussian Process Bandits for Top-k Recommendations NeurIPS 2024 Gaussian Process Optimization for Adaptable Multi-Objective Text Generation using Linearly-Weighted Language Models NAACL 2024 Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels AISTATS 2024 Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure AAAI 2024 Generalization of Hamiltonian algorithms NeurIPS 2024 Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons AAAI 2024 Generalized Schrödinger Bridge Matching ICLR 2024 Generalized Variational Inference via Optimal Transport AAAI 2024 Geometric No-U-Turn Samplers: Concepts and Evaluation PGM 2024 Getting More by Knowing Less: Bayesian Incentive Compatible Mechanisms for Fair Division IJCAI 2024 Gibbs Sampling of Continuous Potentials on a Quantum Computer ICML 2024 G–LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint) AAAI 2024 Globally Convergent Variational Inference NeurIPS 2024 Graph Bayesian Optimization for Multiplex Influence Maximization AAAI 2024 Graphical Multioutput Gaussian Process with Attention ICLR 2024 Graph Neural Networks with a Distribution of Parametrized Graphs ICML 2024 Grey-Box Bayesian Optimization for Sensor Placement in Assisted Living Environments AAAI 2024