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Keyword

hyperparameter learning

Description: A machine learning procedure that estimates or optimizes model hyperparameters, such as kernel choices or prior settings, from data.
OpenAlex ID: keywords/hyperparameter-learning
Gaussian Processes in Machine Learning
2004 · Carl Edward Rasmussen · Lecture notes in computer science · 5,427
Sparse Gaussian Processes using Pseudo-inputs
2005 · Edward Snelson, Zoubin Ghahramani · 1,387
Self-weighted Multiview Clustering with Multiple Graphs
2017 · Feiping Nie, Jing Li, et al. · International Joint Conference on Artificial Intelligence · 611
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Gaussian Processes for Ordinal Regression
2005 · Wei Chu, Zoubin Ghahramani · Cambridge University Engineering Department Publications Database · 422
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
2014 · Jun Fang, Yanning Shen, et al. · IEEE Transactions on Signal Processing · 295
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Hilbert space methods for reduced-rank Gaussian process regression
2019 · Arno Solin, Simo Särkkä · Statistics and Computing · 218
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Additive Gaussian Processes
2011 · Duvenaud, David, Nickisch, Hannes, et al. · arXiv (Cornell University) · 210
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Gaussian Processes for Object Categorization
2009 · Ashish Kapoor, Kristen Grauman, et al. · International Journal of Computer Vision · 179
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Learning a meta-level prior for feature relevance from multiple related tasks
2007 · Su-In Lee, Vassil Chatalbashev, et al. · International Conference on Machine Learning · 166
A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm
2009 · Achim J. Lilienthal, Matteo Reggente, et al. · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 161
Automatic relevance determination for multi‐way models
2009 · Morten Mørup, Lars Kai Hansen · Journal of Chemometrics · 161
Recursive Gaussian process: On-line regression and learning
2014 · Marco F. Huber · Pattern Recognition Letters · 136
Deep Monocular Depth Estimation via Integration of Global and Local Predictions
2018 · Youngjung Kim, Hyungjoo Jung, et al. · IEEE Transactions on Image Processing · 127
Global analytic solution of fully-observed variational Bayesian matrix factorization
2013 · Shinichi Nakajima, Masashi Sugiyama, et al. · 126
Bayesian Gaussian Process Classification with the EM-EP Algorithm
2006 · Hyun-Chul Kim, Z. Ghahramani · IEEE Transactions on Pattern Analysis and Machine Intelligence · 125
Rethinking Bayesian Learning for Data Analysis: The art of prior and inference in sparsity-aware modeling
2022 · Lei Cheng, Feng Yin, et al. · IEEE Signal Processing Magazine · 116
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Semi-supervised learning : from Gaussian fields to Gaussian processes
2018 · Zhu, Xiaojin, Lafferty, John, et al. · 113
Learning with Compressible Priors
2009 · Volkan Cevher · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 103
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Overlapping Mixtures of Gaussian Processes for the data association problem
2011 · Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, et al. · Pattern Recognition · 99
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Sound source localization and speech enhancement with sparse Bayesian learning beamforming
2018 · Angeliki Xenaki, Jesper Bünsow Boldt, et al. · The Journal of the Acoustical Society of America · 93
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Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness
2008 · Christian Plagemann, Kristian Kersting, et al. · Lecture notes in computer science · 93
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Evidence Optimization Techniques for Estimating Stimulus-Response Functions
2002 · Maneesh Sahani, Jennifer F. Linden · UCL Discovery (University College London) · 92
Music Genre and Emotion Recognition Using Gaussian Processes
2014 · Konstantin Markov, Tomoko Matsui · IEEE Access · 91
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Sparse Bayesian Learning for End-to-End EEG Decoding
2023 · Wenlong Wang, Feifei Qi, et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 89
Informative planning and online learning with sparse Gaussian processes
2017 · Kai-Chieh Ma, Lantao Liu, et al. · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 77
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Works count: 315
Citation count: 12,980
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