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Stochastic Variational Deep Kernel Learning

1 November 2016

Papers citing "Stochastic Variational Deep Kernel Learning"

50 / 95 papers shown

Title
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks Charbel Bou Chaaya M. Bennis 64 0 0 08 May 2025
Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization Natalie Maus Kyurae Kim Yimeng Zeng Haydn Thomas Jones Fangping Wan Marcelo Der Torossian Torres Cesar de la Fuente-Nunez Jacob R. Gardner 136 0 0 31 Jan 2025
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise T. Pouplin Alan Jeffares Nabeel Seedat Mihaela van der Schaar 461 3 0 05 Jun 2024
Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets Wenlong Lyu Shoubo Hu Jie Chuai Zhitang Chen 27 2 0 09 Aug 2023
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization Navid Ansari Hans-Peter Seidel Vahid Babaei 61 2 0 01 Jun 2023
Guided Deep Kernel Learning Idan Achituve Gal Chechik Ethan Fetaya BDL 59 7 0 19 Feb 2023
Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting Yunyao Cheng Chenjuan Guo Kai Chen Kai Zhao B. Yang Jiandong Xie Christian S. Jensen Feiteng Huang Kai Zheng AI4TS 72 1 0 20 Dec 2022
Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift Miguel Rios A. Abu-Hanna OOD 56 1 0 01 Dec 2022
Synthetic data enable experiments in atomistic machine learning John L A Gardner Z. Beaulieu Volker L. Deringer 70 9 0 29 Nov 2022
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data N. Botteghi Mengwu Guo C. Brune 100 11 0 27 Aug 2022
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review N. Botteghi M. Poel C. Brune SSL OffRL 88 13 0 27 Aug 2022
Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves Max Lamparth Ludwig M. Böss U. Steinwandel K. Dolag 17 0 0 14 Aug 2022
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors Gianluca Detommaso Alberto Gasparin A. Wilson Cédric Archambeau UQCV BDL 78 3 0 17 Jul 2022
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process Hao Yu Kaiyang Guo Mahdi Karami Xi Chen Guojun Zhang Pascal Poupart FedML 81 3 0 13 Jun 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 73 4 0 15 May 2022
Gaussian Process Self-triggered Policy Search in Weakly Observable Environments Hikaru Sasaki T. Hirabayashi Kaoru Kawabata Takamitsu Matsubara 23 2 0 07 May 2022
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Jiajun He Austin Tripp José Miguel Hernández-Lobato 66 23 0 05 May 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 228 51 0 01 May 2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders Samuel Stanton Wesley J. Maddox Nate Gruver Phillip M. Maffettone E. Delaney Peyton Greenside A. Wilson BDL 77 98 0 23 Mar 2022
Local Latent Space Bayesian Optimization over Structured Inputs Natalie Maus Haydn Thomas Jones Juston Moore Matt J. Kusner John Bradshaw Jacob R. Gardner BDL 118 72 0 28 Jan 2022
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 81 3 0 19 Nov 2021
Toward a `Standard Model' of Machine Learning Zhiting Hu Eric Xing 88 12 0 17 Aug 2021
Personalized Federated Learning with Gaussian Processes Idan Achituve Aviv Shamsian Aviv Navon Gal Chechik Ethan Fetaya FedML 87 103 0 29 Jun 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 73 20 0 21 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
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes Ji Won Park A. Villar Yin Li Yan-Fei Jiang S. Ho J. Lin P. Marshall A. Roodman BDL 43 5 0 02 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 137 133 0 14 May 2021
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking Xingchen Ma Matthew B. Blaschko 85 36 0 10 May 2021
Uncertainty-aware Remaining Useful Life predictor Luca Biggio Alexander Wieland M. A. Chao I. Kastanis Olga Fink AI4CE 29 7 0 08 Apr 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
Calibrated simplex-mapping classification R. Heese J. Schmid Michal Walczak Michael Bortz 50 3 0 04 Mar 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 70 9 0 28 Feb 2021
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification Heng Hao H. Moon Sima Didari J. Woo P. Bangert AI4TS 24 0 0 25 Feb 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
Causal Inference for Time series Analysis: Problems, Methods and Evaluation Raha Moraffah Paras Sheth Mansooreh Karami Anchit Bhattacharya Qianru Wang Anique Tahir A. Raglin Huan Liu CML AI4TS 106 110 0 11 Feb 2021
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
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 71 26 0 22 Oct 2020
Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen Christabella Irwanto Arno Solin UQCV BDL 54 19 0 19 Oct 2020
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 88 11 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
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels Alexander Lavin BDL MedIm 77 9 0 16 Sep 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 88 19 0 11 Aug 2020
Uncertainty Quantification and Deep Ensembles R. Rahaman Alexandre Hoang Thiery UQCV 99 154 0 17 Jul 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 90 116 0 18 Jun 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu Zi Lin Shreyas Padhy Dustin Tran Tania Bedrax-Weiss Balaji Lakshminarayanan UQCV BDL 281 451 0 17 Jun 2020
Deep Latent-Variable Kernel Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang BDL 59 8 0 18 May 2020
How Good are Low-Rank Approximations in Gaussian Process Regression? C. Daskalakis P. Dellaportas A. Panos 60 3 0 03 Apr 2020
Energy-Based Processes for Exchangeable Data Mengjiao Yang Bo Dai H. Dai Dale Schuurmans 74 12 0 17 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 42 0 0 06 Mar 2020