Manifold Gaussian Processes for Regression

24 February 2014

Jan Peters

Papers citing "Manifold Gaussian Processes for Regression"

44 / 44 papers shown

Title
Geometry-aware Active Learning of Spatiotemporal Dynamic Systems Xizhuo Zhang AI4CE 29 0 0 26 Apr 2025
Surrogate-based optimization of system architectures subject to hidden constraints J. Bussemaker P. Saves N. Bartoli T. Lefebvre Björn Nagel AI4CE 128 2 0 11 Apr 2025
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory I. Kavrakov Gledson Rodrigo Tondo Guido Morgenthal AI4CE 51 1 0 21 May 2024
Predictive Churn with the Set of Good Models J. Watson-Daniels Flavio du Pin Calmon Alexander DÁmour Carol Xuan Long David C. Parkes Berk Ustun 83 7 0 12 Feb 2024
Deep Pipeline Embeddings for AutoML Sebastian Pineda Arango Josif Grabocka 28 2 0 23 May 2023
Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees Ewerton R. Vieira A. Sivaramakrishnan Yao Song Edgar Granados Marcio Gameiro Konstantin Mischaikow Ying Hung Kostas E. Bekris AI4CE 30 3 0 04 Oct 2022
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations Andreas Besginow Markus Lange-Hegermann 32 11 0 26 Aug 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 17 6 0 30 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 21 48 0 01 May 2022
Modelling Non-Smooth Signals with Complex Spectral Structure W. Bruinsma Martin Tegnér Richard Turner 22 6 0 14 Mar 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 39 44 0 22 Feb 2022
Maximum Likelihood Uncertainty Estimation: Robustness to Outliers Deebul Nair Nico Hochgeschwender Miguel A. Olivares-Mendez OOD 27 7 0 03 Feb 2022
Dense Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt M. Felsberg F. Khan Martin Danelljan 61 30 0 07 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Personalized Federated Learning with Gaussian Processes Idan Achituve Aviv Shamsian Aviv Navon Gal Chechik Ethan Fetaya FedML 32 98 0 29 Jun 2021
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data Kourosh Hakhamaneshi Pieter Abbeel Vladimir M. Stojanović Aditya Grover 25 10 0 02 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 0 14 May 2021
Deep Gaussian Processes for Few-Shot Segmentation Joakim Johnander Johan Edstedt Martin Danelljan M. Felsberg F. Khan VLM 17 2 0 30 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 0 24 Feb 2021
Neural fidelity warping for efficient robot morphology design Sha Hu Zeshi Yang Greg Mori 47 4 0 08 Dec 2020
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation Alexander I. Cowen-Rivers Wenlong Lyu Rasul Tutunov Zhi Wang Antoine Grosnit ... A. Maraval Hao Jianye Jun Wang Jan Peters H. Ammar 27 74 0 07 Dec 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 19 26 0 22 Oct 2020
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 30 11 0 09 Oct 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 43 19 0 11 Aug 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 25 4 0 21 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 14 437 0 17 Jun 2020
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation Jackie Baek Vivek F. Farias 30 9 0 11 Jun 2020
Discovery of Self-Assembling $π$ -Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation Kirill Shmilovich R. Mansbach Hythem Sidky Olivia E. Dunne S. Panda J. Tovar Andrew L. Ferguson 15 76 0 27 Jan 2020
Online tuning and light source control using a physics-informed Gaussian process Adi A. Hanuka J. Duris J. Shtalenkova Dylan Kennedy A. Edelen Daniel Ratner Xiaobiao Huang 4 20 0 04 Nov 2019
The Differentiable Cross-Entropy Method Brandon Amos Denis Yarats 26 54 0 27 Sep 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 26 0 0 03 Jul 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 27 3 0 31 May 2019
Graph Convolutional Gaussian Processes Ian Walker Ben Glocker GNN 14 35 0 14 May 2019
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach C. Lataniotis S. Marelli Bruno Sudret 23 66 0 15 Dec 2018
Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction Philipp‐Immanuel Schneider Xavier Garcia Santiago V. Soltwisch M. Hammerschmidt Sven Burger C. Rockstuhl 6 88 0 18 Sep 2018
Deep Gaussian Covariance Network K. Cremanns D. Roos BDL 24 20 0 17 Oct 2017
Deep Kernels for Optimizing Locomotion Controllers Rika Antonova Akshara Rai C. Atkeson 25 45 0 27 Jul 2017
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis Tom Nicholson M. Deisenroth J. Hensman 24 111 0 30 May 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni M. Deisenroth BDL GP 17 415 0 24 May 2017
Learning Scalable Deep Kernels with Recurrent Structure Maruan Al-Shedivat A. Wilson Yunus Saatchi Zhiting Hu Eric P. Xing BDL 13 104 0 27 Oct 2016
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units Stefanos Eleftheriadis Ognjen Rudovic M. Deisenroth M. Pantic DRL 25 28 0 16 Aug 2016
Variational Auto-encoded Deep Gaussian Processes Zhenwen Dai Andreas C. Damianou Javier I. González Neil D. Lawrence BDL 24 131 0 19 Nov 2015
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric P. Xing BDL 33 872 0 06 Nov 2015
Variable noise and dimensionality reduction for sparse Gaussian processes Edward Snelson Zoubin Ghahramani 87 79 0 27 Jun 2012