Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 138 papers shown

Title
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective Hechuan Wen Tong Chen Mingming Gong Li Kheng Chai S. Sadiq Hongzhi Yin CML 58 0 0 08 May 2025
Conditional Diffusion-Based Retrieval of Atmospheric CO2 from Earth Observing Spectroscopy William R. Keely Otto Lamminpää Steffen Mauceri Sean M. R. Crowell Christopher W. O'Dell Gregory R. McGarragh 50 0 0 23 Apr 2025
Latent Bayesian Optimization via Autoregressive Normalizing Flows Seunghun Lee Jinyoung Park Jaewon Chu Minseo Yoon H. Kim BDL 30 1 0 21 Apr 2025
GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian Optimization Bojana Ranković P. Schwaller BDL 155 0 0 08 Apr 2025
Image Classification with Deep Reinforcement Active Learning Mingyuan Jiu Xuguang Song H. Sahbi Shupan Li Yan Chen Wei Guo Lihua Guo Mingliang Xu VLM 24 0 0 31 Dec 2024
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data M. Risser M. Noack Hengrui Luo Ronald Pandolfi GP 33 0 0 07 Nov 2024
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning Peter Eckmann D. Wu G. Heinzelmann Michael K. Gilson Rose Yu AI4CE 40 0 0 15 Oct 2024
Federated Neural Nonparametric Point Processes Hui Chen Hengyu Liu Hengyu Liu Xuhui Fan Zhilin Zhao Feng Zhou Christopher J. Quinn Longbing Cao FedML 38 0 0 08 Oct 2024
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning Nils Lehmann Jakob Gawlikowski Adam J. Stewart Vytautas Jancauskas Stefan Depeweg Eric T. Nalisnick N. Gottschling 40 0 0 04 Oct 2024
Deep Kernel Posterior Learning under Infinite Variance Prior Weights Jorge Loría A. Bhadra BDL UQCV 61 0 0 02 Oct 2024
Learning Deep Kernels for Non-Parametric Independence Testing Nathaniel Xu Feng Liu Danica J. Sutherland BDL 31 0 0 10 Sep 2024
Bayesian meta learning for trustworthy uncertainty quantification Zhenyuan Yuan Thinh T. Doan UQCV 25 0 0 27 Jul 2024
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise T. Pouplin Alan Jeffares Nabeel Seedat Mihaela van der Schaar 50 3 0 05 Jun 2024
Recurrent Deep Kernel Learning of Dynamical Systems N. Botteghi Paolo Motta Andrea Manzoni P. Zunino Mengwu Guo 18 1 0 30 May 2024
Infinite-Dimensional Feature Interaction Chenhui Xu Fuxun Yu Maoliang Li Zihao Zheng Zirui Xu Jinjun Xiong Xiang Chen 34 1 0 22 May 2024
Spectral complexity of deep neural networks Simmaco Di Lillo Domenico Marinucci Michele Salvi S. Vigogna BDL 74 1 0 15 May 2024
Kernel PCA for Out-of-Distribution Detection Kun Fang Qinghua Tao Kexin Lv M. He Xiaolin Huang Jie-jin Yang OODD 46 2 0 05 Feb 2024
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach Mahrokh Ghoddousi Boroujeni Andreas Krause Giancarlo Ferrari-Trecate FedML 32 3 0 16 Jan 2024
Hybrid Modeling Design Patterns Maja Rudolph Stefan Kurz Barbara Rakitsch AI4CE 26 8 0 29 Dec 2023
Clairvoyance: A Pipeline Toolkit for Medical Time Series Daniel Jarrett Jinsung Yoon Ioana Bica Zhaozhi Qian A. Ercole M. Schaar AI4TS 21 35 0 28 Oct 2023
Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning Mohamadreza Jafaryani H. Sheikhzadeh V. Pourahmadi 14 4 0 08 Sep 2023
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation Fengxue Zhang Jialin Song James Bowden Alexander Ladd Yisong Yue Thomas A. Desautels Yuxin Chen 25 6 0 25 Jul 2023
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation S. Bouabid Dino Sejdinovic D. Watson‐Parris 9 5 0 14 Jul 2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez Matthias Katzfuss BDL UQCV 58 1 0 26 May 2023
The Representation Jensen-Shannon Divergence J. Hoyos-Osorio Santiago Posso-Murillo L. S. Giraldo 40 6 0 25 May 2023
Inverse Protein Folding Using Deep Bayesian Optimization N. Maus Yimeng Zeng Daniel A. Anderson Phillip M. Maffettone Aaron C. Solomon Peyton Greenside Osbert Bastani Jacob R. Gardner 15 2 0 25 May 2023
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs Pau Batlle Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 24 17 0 08 May 2023
Gaussian process deconvolution Felipe A. Tobar Arnaud Robert Jorge F. Silva 17 5 0 08 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 27 75 0 07 May 2023
Experience-Based Evolutionary Algorithms for Expensive Optimization Xunzhao Yu Yan Wang Ling Zhu Dimitar Filev Xin Yao 9 2 0 09 Apr 2023
Self-Distillation for Gaussian Process Regression and Classification Kenneth Borup L. Andersen 11 2 0 05 Apr 2023
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments Arpan Biswas Yongtao Liu Nicole Creange Yu-Chen Liu S. Jesse Jan-Chi Yang Sergei V. Kalinin M. Ziatdinov Rama K Vasudevan 16 5 0 05 Apr 2023
Deep Ranking Ensembles for Hyperparameter Optimization Abdus Salam Khazi Sebastian Pineda Arango Josif Grabocka BDL 31 7 0 27 Mar 2023
Transfer Learning for Bayesian Optimization: A Survey Tianyi Bai Yang Li Yu Shen Xinyi Zhang Wentao Zhang Bin Cui BDL 34 29 0 12 Feb 2023
Multi-view Kernel PCA for Time series Forecasting Arun Pandey Hannes De Meulemeester B. De Moor Johan A. K. Suykens AI4TS 20 5 0 24 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 26 7 0 21 Jan 2023
Faithful Heteroscedastic Regression with Neural Networks Andrew Stirn H. Wessels Megan D. Schertzer L. Pereira Neville E. Sanjana David A. Knowles UQCV 17 14 0 18 Dec 2022
Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift Miguel Rios A. Abu-Hanna OOD 14 1 0 01 Dec 2022
Synthetic data enable experiments in atomistic machine learning John L A Gardner Z. Beaulieu Volker L. Deringer 29 6 0 29 Nov 2022
Counterfactual Learning with Multioutput Deep Kernels A. Caron G. Baio I. Manolopoulou BDL CML OffRL 15 1 0 20 Nov 2022
Introduction and Exemplars of Uncertainty Decomposition Shuo Chen UD UQCV PER 30 0 0 17 Nov 2022
MMD-B-Fair: Learning Fair Representations with Statistical Testing Namrata Deka Danica J. Sutherland 15 6 0 15 Nov 2022
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice Jonas Rothfuss Martin Josifoski Vincent Fortuin Andreas Krause 35 7 0 14 Nov 2022
Uncertainty Quantification for Atlas-Level Cell Type Transfer J. Engelmann Leon Hetzel Giovanni Palla L. Sikkema Malte D. Luecken Fabian J. Theis 19 3 0 07 Nov 2022
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach Ankush Chakrabarty 17 9 0 31 Oct 2022
Compositional Law Parsing with Latent Random Functions Fan Shi Bin Li Xiangyang Xue CoGe 19 4 0 15 Sep 2022
The Neural Process Family: Survey, Applications and Perspectives Saurav Jha Dong Gong Xuesong Wang Richard E. Turner L. Yao BDL 73 24 0 01 Sep 2022
Fast emulation of density functional theory simulations using approximate Gaussian processes S. Stetzler M. Grosskopf E. Lawrence 13 0 0 24 Aug 2022
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation Zhitong Xiong Haopeng Li Xiao Xiang Zhu 35 35 0 30 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 21 0 0 27 Jun 2022