Scalable Variational Gaussian Process Classification

7 November 2014

Papers citing "Scalable Variational Gaussian Process Classification"

50 / 337 papers shown

Title
Last Layer Marginal Likelihood for Invariance Learning Pola Schwobel Martin Jørgensen Sebastian W. Ober Mark van der Wilk BDL UQCV 90 30 0 14 Jun 2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes Sanyam Kapoor Marc Finzi Ke Alexander Wang A. Wilson 74 12 0 12 Jun 2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition Shengyang Sun Jiaxin Shi A. Wilson Roger C. Grosse BDL 31 7 0 10 Jun 2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features Thomas M. McDonald Mauricio A. Alvarez 85 10 0 10 Jun 2021
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data Jens Petersen Gregor Koehler David Zimmerer Fabian Isensee Paul F. Jäger Klaus H. Maier-Hein BDL AI4TS 71 3 0 09 Jun 2021
Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems Yinchong Yang Florian Buettner BDL 30 6 0 08 Jun 2021
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes Veit Wild Motonobu Kanagawa Dino Sejdinovic 81 16 0 02 Jun 2021
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning Zhiliang Wu Yinchong Yang Jindong Gu Volker Tresp UQCV MedIm 48 9 0 01 Jun 2021
Gaussian Processes with Differential Privacy Antti Honkela Laila Melkas 61 2 0 01 Jun 2021
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data Shixiang Zhu Alexander W. Bukharin Liyan Xie Khurram Yamin Shihao Yang P. Keskinocak Yao Xie 37 8 0 31 May 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 60 6 0 24 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir J. Hensman Mark van der Wilk Carl Henrik Ek Zoubin Ghahramani N. Durrande BDL UQCV 103 31 0 10 May 2021
Mixtures of Gaussian Processes for regression under multiple prior distributions Sarem Seitz 10 1 0 19 Apr 2021
GPflux: A Library for Deep Gaussian Processes Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried A. Artemev Mark van der Wilk J. Hensman M. Deisenroth S. T. John GP 86 23 0 12 Apr 2021
Uncertainty-aware Remaining Useful Life predictor Luca Biggio Alexander Wieland M. A. Chao I. Kastanis Olga Fink AI4CE 27 7 0 08 Apr 2021
Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression Ehsan Zobeidi Alec Koppel Nikolay Atanasov 61 13 0 30 Mar 2021
Sparse Algorithms for Markovian Gaussian Processes William J. Wilkinson Arno Solin Vincent Adam 52 12 0 19 Mar 2021
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey Kai Chen Qinglei Kong Yijue Dai Yue Xu Feng Yin Lexi Xu Shuguang Cui 102 30 0 18 Mar 2021
Generative Particle Variational Inference via Estimation of Functional Gradients Neale Ratzlaff Qinxun Bai Fuxin Li Wenyuan Xu BDL DRL 114 0 0 01 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 82 109 0 24 Feb 2021
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty Joost R. van Amersfoort Lewis Smith Andrew Jesson Oscar Key Y. Gal UQCV 79 104 0 22 Feb 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 74 19 0 16 Feb 2021
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning Idan Achituve Aviv Navon Yochai Yemini Gal Chechik Ethan Fetaya GP 61 34 0 15 Feb 2021
Healing Products of Gaussian Processes Samuel N. Cohen R. Mbuvha T. Marwala M. Deisenroth GP 43 0 0 14 Feb 2021
Variational Bayes survival analysis for unemployment modelling P. Boškoski M. Perne M. Ramesa Biljana Mileva-Boshkoska CML 67 10 0 03 Feb 2021
Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells Luka Žnidarič G. Nusev B. Morel J. Mougin D. Juricic P. Boškoski 40 9 0 20 Jan 2021
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks Shiwei Lan Shuyi Li Babak Shahbaba UQCV BDL 84 16 0 11 Jan 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 127 51 0 27 Dec 2020
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning Chao Du Zhifeng Gao Shuo Yuan Lining Gao Z. Li Yifan Zeng Xiaoqiang Zhu Jian Xu Kun Gai Kuang-chih Lee 87 18 0 25 Nov 2020
Probabilistic modeling of discrete structural response with application to composite plate penetration models Anindya Bhaduri C. Meyer J. Gillespie B. Haque Michael D. Shields L. Graham‐Brady 44 9 0 23 Nov 2020
A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters Sim Kuan Goh N. P. Singh Zhi Jun Lim Sameer Alam 24 9 0 18 Nov 2020
Sparse within Sparse Gaussian Processes using Neighbor Information Gia-Lac Tran Dimitrios Milios Pietro Michiardi Maurizio Filippone 43 16 0 10 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 102 61 0 08 Nov 2020
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? Chaoqi Wang Shengyang Sun Roger C. Grosse UQCV 61 25 0 06 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 103 34 0 03 Nov 2020
DRF: A Framework for High-Accuracy Autonomous Driving Vehicle Modeling Shu Jiang Yu Wang Longtao Lin Weiman Lin Yu Cao Jinghao Miao Qi Luo 28 2 0 01 Nov 2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes Tim G. J. Rudner Oscar Key Y. Gal Tom Rainforth 18 3 0 01 Nov 2020
Inter-domain Deep Gaussian Processes Tim G. J. Rudner Dino Sejdinovic Yarin Gal 79 11 0 01 Nov 2020
Ranking Creative Language Characteristics in Small Data Scenarios Julia Siekiera M. Koppel Edwin Simpson Kevin Stowe Iryna Gurevych Stefan Kramer BDL 13 3 0 23 Oct 2020
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 102 7 0 20 Oct 2020
Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen Christabella Irwanto Arno Solin UQCV BDL 54 19 0 19 Oct 2020
Probabilistic selection of inducing points in sparse Gaussian processes Anders Kirk Uhrenholt V. Charvet B. S. Jensen 23 13 0 19 Oct 2020
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models E. Zelikman Sharon Zhou Jeremy Irvin Cooper D. Raterink Hao Sheng Anand Avati Jack Kelly Ram Rajagopal A. Ng D. Gagne 63 12 0 09 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning A. Tompkins Rafael Oliveira F. Ramos 60 6 0 09 Oct 2020
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence Agustinus Kristiadi Matthias Hein Philipp Hennig BDL 63 9 0 06 Oct 2020
Recyclable Gaussian Processes P. Moreno-Muñoz Antonio Artés-Rodríguez Mauricio A. Alvarez BDL 39 1 0 06 Oct 2020
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels Alexander Lavin BDL MedIm 77 9 0 16 Sep 2020
Generalized Multi-Output Gaussian Process Censored Regression Daniele Gammelli Kasper Pryds Rolsted Dario Pacino Filipe Rodrigues 43 14 0 10 Sep 2020
Information Theoretic Meta Learning with Gaussian Processes Michalis K. Titsias Francisco J. R. Ruiz Sotirios Nikoloutsopoulos Alexandre Galashov FedML 99 15 0 07 Sep 2020
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables Q. Wang H. V. Hoof BDL 66 42 0 21 Aug 2020