Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Kernel Interpolation for Scalable Online Gaussian Processes Samuel Stanton Wesley J. Maddox Ian A. Delbridge A. Wilson GP 65 30 0 02 Mar 2021
Fast Adaptation with Linearized Neural Networks Wesley J. Maddox Shuai Tang Pablo G. Moreno A. Wilson Andreas C. Damianou 92 32 0 02 Mar 2021
Neural Generalization of Multiple Kernel Learning Ahamad Navid Ghanizadeh Kamaledin Ghiasi-Shirazi R. Monsefi Mohammadreza Qaraei 25 2 0 26 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 81 105 0 22 Feb 2021
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting I. Hawryluk H. Hoeltgebaum Swapnil Mishra Xenia Miscouridou R. Schnekenberg C. Whittaker Michaela Vollmer Seth Flaxman Samir Bhatt T. Mellan 33 16 0 22 Feb 2021
Risk factor identification for incident heart failure using neural network distillation and variable selection Yikuan Li Shishir Rao M. Mamouei G. Salimi-Khorshidi D. Canoy A. Hassaine Thomas Lukasiewicz Kazem Rahimi 23 0 0 17 Feb 2021
DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou Moksh Jain Hadi Nekoei V. Butoi Paul Bertin Jarrid Rector-Brooks Maksym Korablyov Yoshua Bengio PER UQLM UQCV UD 321 94 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 64 34 0 15 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 113 110 0 11 Feb 2021
Attentive Gaussian processes for probabilistic time-series generation Kuilin Chen Chi-Guhn Lee AI4TS 23 1 0 10 Feb 2021
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System D. Chang O. Frieder C. Hung Hao-Ren Yao 20 2 0 04 Feb 2021
Few-Shot Bayesian Optimization with Deep Kernel Surrogates Martin Wistuba Josif Grabocka BDL 104 70 0 19 Jan 2021
Transferring model structure in Bayesian transfer learning for Gaussian process regression Milan Papez A. Quinn 41 12 0 18 Jan 2021
Sensitivity Prewarping for Local Surrogate Modeling Nathan Wycoff M. Binois R. Gramacy 63 10 0 15 Jan 2021
A Novel Regression Loss for Non-Parametric Uncertainty Optimization Joachim Sicking Maram Akila Maximilian Pintz Tim Wirtz Asja Fischer Stefan Wrobel UQCV 31 3 0 07 Jan 2021
Wasserstein Dropout Joachim Sicking Maram Akila Maximilian Pintz Tim Wirtz Asja Fischer Stefanie Wrobel BDL OOD UQCV 44 1 0 23 Dec 2020
Guiding Neural Network Initialization via Marginal Likelihood Maximization Anthony S. Tai Chunfeng Huang 22 0 0 17 Dec 2020
Kernelized Classification in Deep Networks Sadeep Jayasumana Srikumar Ramalingam Sanjiv Kumar 41 4 0 08 Dec 2020
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning Hongseok Namkoong Sam Daulton E. Bakshy OffRL 46 7 0 29 Nov 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 96 18 0 25 Nov 2020
Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality Morgane Austern Vasilis Syrgkanis 79 4 0 23 Nov 2020
Reducing the Variance of Variational Estimates of Mutual Information by Limiting the Critic's Hypothesis Space to RKHS P. A. Sreekar Ujjwal Tiwari A. Namboodiri 39 2 0 17 Nov 2020
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning Hany Abdulsamad Peter Nickl Pascal Klink Jan Peters 23 3 0 10 Nov 2020
Learning and Evaluating Representations for Deep One-class Classification Kihyuk Sohn Chun-Liang Li Jinsung Yoon Minho Jin Tomas Pfister SSL 177 202 0 04 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 105 34 0 03 Nov 2020
Hierarchical Gaussian Processes with Wasserstein-2 Kernels S. Popescu D. Sharp James H. Cole Ben Glocker 74 5 0 28 Oct 2020
Are wider nets better given the same number of parameters? A. Golubeva Behnam Neyshabur Guy Gur-Ari 112 44 0 27 Oct 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 89 29 0 26 Oct 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
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit Ben Adlam Jaehoon Lee Lechao Xiao Jeffrey Pennington Jasper Snoek UQCV BDL 74 16 0 14 Oct 2020
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 98 11 0 09 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning A. Tompkins Rafael Oliveira F. Ramos 63 6 0 09 Oct 2020
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels Alexander Lavin BDL MedIm 79 9 0 16 Sep 2020
End-to-end Kernel Learning via Generative Random Fourier Features Kun Fang Fanghui Liu Xiaolin Huang Jie Yang 49 9 0 10 Sep 2020
Information Theoretic Meta Learning with Gaussian Processes Michalis K. Titsias Francisco J. R. Ruiz Sotirios Nikoloutsopoulos Alexandre Galashov FedML 106 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
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 88 19 0 11 Aug 2020
Do ideas have shape? Idea registration as the continuous limit of artificial neural networks H. Owhadi 153 14 0 10 Aug 2020
DeepNNK: Explaining deep models and their generalization using polytope interpolation Sarath Shekkizhar Antonio Ortega 30 6 0 20 Jul 2020
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake C. Snell R. Zemel 109 63 0 20 Jul 2020
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach Yun Yuan Qinzheng Wang X. Yang 67 10 0 17 Jul 2020
Kernel Stein Generative Modeling Wei-Cheng Chang Chun-Liang Li Youssef Mroueh Yiming Yang DiffM BDL 120 5 0 06 Jul 2020
Deep Bayesian Quadrature Policy Optimization Akella Ravi Tej Kamyar Azizzadenesheli Mohammad Ghavamzadeh Anima Anandkumar Yisong Yue 60 5 0 28 Jun 2020
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks Sujay Thakur Cooper Lorsung Yaniv Yacoby Finale Doshi-Velez Weiwei Pan BDL UQCV 90 4 0 21 Jun 2020
Online Kernel based Generative Adversarial Networks Yeojoon Youn Neil Thistlethwaite Sang Keun Choe Jacob D. Abernethy GAN 31 2 0 19 Jun 2020
Bayesian active learning for production, a systematic study and a reusable library Parmida Atighehchian Frederic Branchaud-Charron Alexandre Lacoste 65 26 0 17 Jun 2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Austin Tripp Erik A. Daxberger José Miguel Hernández-Lobato MedIm 103 142 0 16 Jun 2020
NP-PROV: Neural Processes with Position-Relevant-Only Variances Xuesong Wang Lina Yao Xianzhi Wang Feiping Nie BDL 61 3 0 15 Jun 2020
Approximate Inference for Spectral Mixture Kernel Yohan Jung Kyungwoo Song Jinkyoo Park BDL 23 2 0 12 Jun 2020