Deep Kernel Learning

6 November 2015

Papers citing "Deep Kernel Learning"

50 / 504 papers shown

Title
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes Neta Glazer David Chernin Idan Achituve Sharon Gannot Ethan Fetaya 37 0 0 29 May 2025
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks Dongwoo Lee Dong Bok Lee Steven Adriaensen Juho Lee Sung Ju Hwang Frank Hutter Seon Joo Kim Hae Beom Lee BDL 69 0 0 29 May 2025
Training NTK to Generalize with KARE Johannes Schwab Bryan Kelly Semyon Malamud Teng Andrea Xu 146 0 0 16 May 2025
Scaling Gaussian Process Regression with Full Derivative Observations Daniel Huang BDL GP 62 0 0 14 May 2025
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 86 0 0 08 May 2025
CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting Minhyuk Lee HyeKyung Yoon MyungJoo Kang AI4TS 153 0 0 04 May 2025
Evaluating Uncertainty in Deep Gaussian Processes Matthijs van der Lende Jeremias Lino Ferrao Niclas Müller-Hof UQCV 70 0 0 24 Apr 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 218 0 0 23 Apr 2025
Latent Bayesian Optimization via Autoregressive Normalizing Flows Seunghun Lee Jinyoung Park Jaewon Chu Minseo Yoon H. Kim BDL 88 2 0 21 Apr 2025
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks Georgios Makrygiorgos Joshua Hang Sai Ip Ali Mesbah BDL 88 1 0 14 Apr 2025
CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines Ritik Mishra M. Akhtar Muhammad Tanveer 68 0 0 12 Apr 2025
Gradient-based Sample Selection for Faster Bayesian Optimization Qiyu Wei Haowei Wang Zirui Cao Songhao Wang Richard Allmendinger Mauricio A Álvarez 89 1 0 10 Apr 2025
GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian Optimization Bojana Ranković P. Schwaller BDL 483 1 0 08 Apr 2025
Outlook Towards Deployable Continual Learning for Particle Accelerators Kishansingh Rajput Sen Lin Auralee Edelen Willem Blokland Malachi Schram 71 0 0 04 Apr 2025
Sparse Gaussian Neural Processes Tommy Rochussen Vincent Fortuin BDL UQCV 195 0 0 02 Apr 2025
Why risk matters for protein binder design Tudor-Stefan Cotet Igor Krawczuk 114 0 0 31 Mar 2025
Squared families: Searching beyond regular probability models Russell Tsuchida Jiawei Liu Cheng Soon Ong Dino Sejdinovic 68 0 0 27 Mar 2025
Offline Model-Based Optimization: Comprehensive Review Minsu Kim Jiayao Gu Ye Yuan Taeyoung Yun Ziqiang Liu Yoshua Bengio Can Chen OffRL 121 4 0 21 Mar 2025
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes Nima Negarandeh Carlos Mora Ramin Bostanabad 101 0 0 18 Mar 2025
Bayesian Kernel Regression for Functional Data Minoru Kusaba Megumi Iwayama Ryo Yoshida 81 0 0 17 Mar 2025
Deep Learning is Not So Mysterious or Different Andrew Gordon Wilson 101 6 0 03 Mar 2025
Uncertainty-aware abstention in medical diagnosis based on medical texts Artem Vazhentsev Ivan Sviridov Alvard Barseghyan Gleb Kuzmin Alexander Panchenko A. Nesterov Artem Shelmanov Maxim Panov 137 0 0 25 Feb 2025
Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs Akshay Kudva Wei-Ting Tang J. Paulson 68 0 0 19 Feb 2025
Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators Naichang Ke Ryogo Tanaka Yoshinobu Kawahara 86 0 0 06 Jan 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 56 1 0 31 Dec 2024
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods Yuanzhe Tao Huizhuo Yuan Xun Zhou Yuan Cao Q. Gu ODL 66 0 0 27 Dec 2024
High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces Quoc-Anh Hoang Nguyen The Hung Tran 122 2 0 21 Dec 2024
Uncertainty separation via ensemble quantile regression Navid Ansari Hans-Peter Seidel Vahid Babaei UD UQCV 127 0 0 18 Dec 2024
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression Junliang Lyu Yixuan Zhang Xiaoling Lu Feng Zhou FedML 139 1 0 14 Dec 2024
DKMGP: A Gaussian Process Approach to Multi-Task and Multi-Step Vehicle Dynamics Modeling in Autonomous Racing Jingyun Ning Madhur Behl 157 0 0 20 Nov 2024
Inversion-based Latent Bayesian Optimization Jaewon Chu Jinyoung Park Seunghun Lee H. Kim BDL 37 4 0 08 Nov 2024
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data M. Risser M. Noack Hengrui Luo Ronald Pandolfi GP 99 0 0 07 Nov 2024
Deep Q-Exponential Processes Zhi Chang Chukwudi Obite Shuang Zhou Shiwei Lan BDL 69 0 0 29 Oct 2024
Likelihood approximations via Gaussian approximate inference Thang D. Bui 66 0 0 28 Oct 2024
Practical Bayesian Algorithm Execution via Posterior Sampling Chu Xin Cheng Raul Astudillo Thomas Desautels Yisong Yue 67 0 0 27 Oct 2024
A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx Shrenik Zinage Ilias Bilionis Peter Meckl 41 0 0 24 Oct 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 125 0 0 15 Oct 2024
A resource-efficient model for deep kernel learning Luisa DÁmore 41 0 0 13 Oct 2024
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines Edward Milsom Ben Anson Laurence Aitchison 57 0 0 08 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 105 0 0 08 Oct 2024
Online scalable Gaussian processes with conformal prediction for guaranteed coverage Jinwen Xu Qin Lu G. Giannakis 52 1 0 07 Oct 2024
Nonstationary Sparse Spectral Permanental Process Zicheng Sun Yixuan Zhang Zenan Ling Xuhui Fan Feng Zhou 50 0 0 04 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 110 0 0 04 Oct 2024
Measurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments Boris N. Slautin Yu Liu Jan Dec Vladimir V. Shvartsman Doru C. Lupascu M. Ziatdinov Sergei V. Kalinin 108 0 0 03 Oct 2024
Deep Kernel Posterior Learning under Infinite Variance Prior Weights Jorge Loría A. Bhadra BDL UQCV 127 0 0 02 Oct 2024
End-to-End Conformal Calibration for Optimization Under Uncertainty Christopher Yeh Nicolas H. Christianson Alan Wu Adam Wierman Yisong Yue 116 5 0 30 Sep 2024
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions Arpan Biswas Rama Vasudevan Rohit Pant Ichiro Takeuchi Hiroshi Funakubo Yongtao Liu 94 0 0 18 Sep 2024
Learning Representations for Independence Testing Nathaniel Xu Feng Liu Danica J. Sutherland BDL 103 0 0 10 Sep 2024
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling Jian Xu Zhiqi Lin Shigui Li Min Chen Junmei Yang Delu Zeng John Paisley BDL 61 0 0 07 Aug 2024
Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps Yifan Zhu Pranay Thangeda Erica Tevere Ashish Goel Erik Kramer Hari Nayar Melkior Ornik Kris K. Hauser 74 0 0 06 Aug 2024