GPflow: A Gaussian process library using TensorFlow

27 October 2016

Papers citing "GPflow: A Gaussian process library using TensorFlow"

50 / 269 papers shown

Title
Bayesian Optimization using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab GP 22 69 0 07 May 2019
Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data Mark-Oliver Stehr P. Avar Andrew R. Korte Lida Parvin Ziad J. Sahab ... Brian M. Davis Christine A. Morton Christopher J. Sevinsky M. Zavodszky A. Vertes AI4TS CML 11 5 0 06 May 2019
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features Arno Solin Manon Kok 18 23 0 10 Apr 2019
Robust Deep Gaussian Processes Jeremias Knoblauch GP 30 17 0 04 Apr 2019
Generalized Variational Inference: Three arguments for deriving new Posteriors Jeremias Knoblauch Jack Jewson Theodoros Damoulas DRL BDL 39 105 0 03 Apr 2019
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints Nimish Awalgaonkar Ilias Bilionis Xiaoqi Liu P. Karava Athanasios Tzempelikos AI4TS AI4CE 38 2 0 21 Mar 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 24 226 0 19 Mar 2019
Deep Gaussian Processes for Multi-fidelity Modeling Kurt Cutajar Mark Pullin Andreas C. Damianou Neil D. Lawrence Javier I. González AI4CE 30 109 0 18 Mar 2019
Functional Regularisation for Continual Learning with Gaussian Processes Michalis K. Titsias Jonathan Richard Schwarz A. G. Matthews Razvan Pascanu Yee Whye Teh CLL BDL 17 184 0 31 Jan 2019
Meta-Learning Mean Functions for Gaussian Processes Vincent Fortuin Heiko Strathmann Gunnar Rätsch BDL FedML MLT 26 29 0 23 Jan 2019
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language Jamie Fairbrother Christopher Nemeth M. Rischard Johanni Brea Thomas Pinder GP VLM 32 24 0 21 Dec 2018
Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran Michael W. Dusenberry Mark van der Wilk Danijar Hafner UQCV BDL 27 121 0 10 Dec 2018
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 27 55 0 27 Nov 2018
Neural Non-Stationary Spectral Kernel Sami Remes Markus Heinonen Samuel Kaski BDL 18 9 0 27 Nov 2018
Large-scale Heteroscedastic Regression via Gaussian Process Haitao Liu Yew-Soon Ong Jianfei Cai BDL 21 26 0 03 Nov 2018
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain Pablo A. Alvarado Mauricio A. Alvarez D. Stowell 6 7 0 30 Oct 2018
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds David Reeb Andreas Doerr S. Gerwinn Barbara Rakitsch GP 13 33 0 29 Oct 2018
Data Association with Gaussian Processes Markus Kaiser Clemens Otte Thomas Runkler Carl Henrik Ek 21 0 0 16 Oct 2018
Tuning Fairness by Balancing Target Labels T. Kehrenberg Zexun Chen Novi Quadrianto 25 4 0 12 Oct 2018
Harmonizable mixture kernels with variational Fourier features Zheyan Shen Markus Heinonen Samuel Kaski 33 17 0 10 Oct 2018
Deep learning with differential Gaussian process flows Pashupati Hegde Markus Heinonen Harri Lähdesmäki Samuel Kaski BDL 26 42 0 09 Oct 2018
Deep convolutional Gaussian processes Kenneth Blomqvist Samuel Kaski Markus Heinonen BDL 33 60 0 06 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 8 1,077 0 28 Sep 2018
Orthogonally Decoupled Variational Gaussian Processes Hugh Salimbeni Ching-An Cheng Byron Boots M. Deisenroth 19 43 0 24 Sep 2018
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes Linfeng Liu Liping Liu BDL 23 0 0 08 Sep 2018
Bayesian Nonparametric Spectral Estimation Felipe A. Tobar 19 29 0 06 Sep 2018
Multi-Output Convolution Spectral Mixture for Gaussian Processes Kai Chen Twan van Laarhoven P. Groot Jinsong Chen E. Marchiori 20 11 0 07 Aug 2018
Multitask Gaussian Process with Hierarchical Latent Interactions Kai Chen Twan van Laarhoven E. Marchiori Feng Yin Shuguang Cui 17 6 0 03 Aug 2018
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes Christian Donner Manfred Opper 43 35 0 02 Aug 2018
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization Dipti Jasrasaria Edward O. Pyzer-Knapp 21 21 0 03 Jul 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 33 683 0 03 Jul 2018
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data Martin Wistuba Ambrish Rawat BDL 30 2 0 07 Jun 2018
Grouped Gaussian Processes for Solar Power Prediction A. Dahl Edwin V. Bonilla 8 20 0 07 Jun 2018
Variational Implicit Processes Chao Ma Yingzhen Li José Miguel Hernández-Lobato BDL 24 68 0 06 Jun 2018
Deep Gaussian Processes with Convolutional Kernels Vinayak Kumar Vaibhav Singh P. K. Srijith Andreas C. Damianou BDL GP 33 29 0 05 Jun 2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification Dimitrios Milios Raffaello Camoriano Pietro Michiardi Lorenzo Rosasco Maurizio Filippone UQCV 35 74 0 28 May 2018
Efficient Inference in Multi-task Cox Process Models Virginia Aglietti Theodoros Damoulas Edwin V. Bonilla 11 8 0 24 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 88 550 0 30 Apr 2018
Posterior Inference for Sparse Hierarchical Non-stationary Models K. Monterrubio-Gómez L. Roininen S. Wade Theo Damoulas Mark Girolami 37 27 0 04 Apr 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 30 85 0 24 Mar 2018
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss Jacob R. Gardner Kilian Q. Weinberger A. Wilson 25 94 0 16 Mar 2018
Variational zero-inflated Gaussian processes with sparse kernels Pashupati Hegde Markus Heinonen Samuel Kaski 19 5 0 13 Mar 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling C. Riquelme George Tucker Jasper Snoek BDL 41 365 0 26 Feb 2018
Product Kernel Interpolation for Scalable Gaussian Processes Jacob R. Gardner Geoff Pleiss Ruihan Wu Kilian Q. Weinberger A. Wilson 35 71 0 24 Feb 2018
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus Adam D. Cobb Richard Everett Andrew Markham Stephen J. Roberts 10 7 0 22 Feb 2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation F. Wenzel Théo Galy-Fajou Christian Donner Marius Kloft Manfred Opper 34 36 0 18 Feb 2018
Few-shot learning of neural networks from scratch by pseudo example optimization Akisato Kimura Zoubin Ghahramani Koh Takeuchi Tomoharu Iwata N. Ueda 35 52 0 08 Feb 2018
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms Ahmed Alaa Mihaela van der Schaar CML 18 42 0 24 Dec 2017
GPflowOpt: A Bayesian Optimization Library using TensorFlow Nicolas Knudde J. Herten T. Dhaene Ivo Couckuyt GP 25 78 0 10 Nov 2017
Fidelity-Weighted Learning Mostafa Dehghani Arash Mehrjou Stephan Gouws J. Kamps Bernhard Schölkopf NoLa FedML 44 75 0 08 Nov 2017