Showing 1–20 of 153 results
/ Date/ Name
Aug 3, 2022Flow Annealed Importance Sampling BootstrapOct 10, 2022Sampling-based inference for large linear models, with application to linearised LaplaceFeb 18, 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural NetworksJun 6, 2017Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical SpaceMar 4, 2024A Generative Model of Symmetry TransformationsJul 12, 2023Online Laplace Model Selection RevisitedOct 13, 2023Genetic algorithms are strong baselines for molecule generationOct 30, 2023Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological imagesOct 20, 2021Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit DifferentiationDec 10, 2011Convergent Expectation Propagation in Linear Models with Spike-and-slab PriorsNov 10, 2015Black-box $α$-divergence MinimizationJun 26, 2023Leveraging Task Structures for Improved Identifiability in Neural Network RepresentationsFeb 18, 2015Predictive Entropy Search for Bayesian Optimization with Unknown ConstraintsNov 30, 2015A General Framework for Constrained Bayesian Optimization using Information-based SearchOct 13, 2023Retro-fallback: retrosynthetic planning in an uncertain worldJun 20, 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient DescentJun 26, 2023Tanimoto Random Features for Scalable Molecular Machine LearningMay 18, 2013Dynamic Covariance Models for Multivariate Financial Time SeriesJul 1, 2013Gaussian Process Conditional Copulas with Applications to Financial Time SeriesAug 23, 2023Graph Neural Stochastic Differential Equations