Doubly Stochastic Variational Inference for Deep Gaussian Processes

24 May 2017

Papers citing "Doubly Stochastic Variational Inference for Deep Gaussian Processes"

50 / 230 papers shown

Title
Stochastic Differential Equations with Variational Wishart Diffusions Martin Jørgensen M. Deisenroth Hugh Salimbeni DiffM 12 8 0 26 Jun 2020
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 26 10 0 18 Jun 2020
Calibrated Reliable Regression using Maximum Mean Discrepancy Peng Cui Wenbo Hu Jun Zhu UQCV 8 47 0 18 Jun 2020
A Deterministic Approximation to Neural SDEs Andreas Look M. Kandemir Barbara Rakitsch Jan Peters DiffM 4 4 0 16 Jun 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties J. Lindinger David Reeb C. Lippert Barbara Rakitsch BDL UQCV 25 8 0 22 May 2020
Deep Latent-Variable Kernel Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang BDL 12 8 0 18 May 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 26 60 0 17 May 2020
Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit Tomoki Koriyama Hiroshi Saruwatari BDL 21 5 0 22 Apr 2020
Advances in Bayesian Probabilistic Modeling for Industrial Applications Sayan Ghosh Piyush Pandita Steven Atkinson W. Subber Yiming Zhang Natarajan Chennimalai-Kumar S. Chakrabarti Liping Wang AI4CE 9 30 0 26 Mar 2020
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records Yikuan Li Shishir Rao A. Hassaine R. Ramakrishnan Yajie Zhu D. Canoy G. Salimi-Khorshidi Thomas Lukasiewicz K. Rahimi BDL UQCV 16 36 0 23 Mar 2020
Energy-Based Processes for Exchangeable Data Mengjiao Yang Bo Dai H. Dai Dale Schuurmans 22 12 0 17 Mar 2020
Amortized variance reduction for doubly stochastic objectives Ayman Boustati Sattar Vakili J. Hensman S. T. John 24 5 0 09 Mar 2020
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi Markus Heinonen Edwin V. Bonilla Zheyan Shen Maurizio Filippone UQCV BDL 14 0 0 06 Mar 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 40 94 0 02 Mar 2020
Time Series Data Augmentation for Deep Learning: A Survey Qingsong Wen Liang Sun Fan Yang Xiaomin Song Jing Gao Xue Wang Huan Xu AI4TS 32 635 0 27 Feb 2020
Deep Sigma Point Processes M. Jankowiak Geoff Pleiss Jacob R. Gardner BDL 19 21 0 21 Feb 2020
Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou Yitong Li Yuan Wu David Carlson UQCV 30 15 0 12 Feb 2020
Graph Convolutional Gaussian Processes For Link Prediction Felix L. Opolka Pietro Lió GNN 27 15 0 11 Feb 2020
Conditional Deep Gaussian Processes: multi-fidelity kernel learning Chi-Ken Lu Patrick Shafto 19 5 0 07 Feb 2020
Transport Gaussian Processes for Regression Gonzalo Rios GP 13 6 0 30 Jan 2020
Doubly Sparse Variational Gaussian Processes Vincent Adam Stefanos Eleftheriadis N. Durrande A. Artemev J. Hensman 19 24 0 15 Jan 2020
Bayesian task embedding for few-shot Bayesian optimization Steven Atkinson Sayan Ghosh Natarajan Chennimalai-Kumar Genghis Khan Liping Wang BDL 16 1 0 02 Jan 2020
Warped Input Gaussian Processes for Time Series Forecasting David Tolpin AI4TS 23 2 0 05 Dec 2019
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO Pablo Morales-Álvarez Pablo Ruiz S. Coughlin Rafael Molina Aggelos K. Katsaggelos 21 14 0 05 Nov 2019
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 19 42 0 26 Oct 2019
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue Raed Al Kontar 107 3 0 15 Oct 2019
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 32 93 0 14 Oct 2019
Deep Kernels with Probabilistic Embeddings for Small-Data Learning Ankur Mallick Chaitanya Dwivedi B. Kailkhura Gauri Joshi T. Y. Han BDL UQCV 12 7 0 13 Oct 2019
On the expected behaviour of noise regularised deep neural networks as Gaussian processes Arnu Pretorius Herman Kamper Steve Kroon 16 9 0 12 Oct 2019
PAC-Bayesian Bounds for Deep Gaussian Processes R. Foll Ingo Steinwart BDL 17 1 0 22 Sep 2019
Compositional uncertainty in deep Gaussian processes Ivan Ustyuzhaninov Ieva Kazlauskaite Markus Kaiser Erik Bodin Neill D. F. Campbell Carl Henrik Ek UQCV 20 22 0 17 Sep 2019
Deep kernel learning for integral measurements Carl Jidling J. Hendriks Thomas B. Schon A. Wills 20 7 0 04 Sep 2019
Structured Variational Inference in Unstable Gaussian Process State Space Models Silvan Melchior Sebastian Curi Felix Berkenkamp Andreas Krause 17 4 0 16 Jul 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 29 0 0 03 Jul 2019
Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck Theodoros Damoulas Kangrui Wang Mark Girolami 20 38 0 19 Jun 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 33 30 0 13 Jun 2019
Deep Compositional Spatial Models A. Zammit‐Mangion T. L. J. Ng Quan Vu Maurizio Filippone 23 55 0 06 Jun 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 27 3 0 31 May 2019
Monotonic Gaussian Process Flow Ivan Ustyuzhaninov Ieva Kazlauskaite Carl Henrik Ek Neill D. F. Campbell 8 14 0 30 May 2019
Non-linear Multitask Learning with Deep Gaussian Processes Ayman Boustati Theodoros Damoulas R. Savage BDL 14 5 0 29 May 2019
Scalable Training of Inference Networks for Gaussian-Process Models Jiaxin Shi Mohammad Emtiyaz Khan Jun Zhu BDL 21 18 0 27 May 2019
Interpretable deep Gaussian processes with moments Chi-Ken Lu Scott Cheng-Hsin Yang Xiaoran Hao Patrick Shafto 18 19 0 27 May 2019
Learning spectrograms with convolutional spectral kernels Zheyan Shen Markus Heinonen Samuel Kaski 9 9 0 23 May 2019
Deep Gaussian Processes with Importance-Weighted Variational Inference Hugh Salimbeni Vincent Dutordoir J. Hensman M. Deisenroth BDL 18 43 0 14 May 2019
Bayesian Optimization using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab GP 14 69 0 07 May 2019
Robust Deep Gaussian Processes Jeremias Knoblauch GP 14 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
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 8 225 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 16 109 0 18 Mar 2019
Functional Variational Bayesian Neural Networks Shengyang Sun Guodong Zhang Jiaxin Shi Roger C. Grosse BDL 22 235 0 14 Mar 2019