Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

3 March 2015

Papers citing "Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)"

50 / 75 papers shown

Title
GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian Optimization Bojana Ranković P. Schwaller BDL 172 0 0 08 Apr 2025
Preconditioned Additive Gaussian Processes with Fourier Acceleration Theresa Wagner Tianshi Xu Franziska Nestler Yuanzhe Xi Martin Stoll 51 1 0 01 Apr 2025
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes Yunyue Wei Vincent Zhuang Saraswati Soedarmadji Yanan Sui 132 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
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation Chris Camaño Daniel Huang BDL GP 45 1 0 28 Oct 2024
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure Jihao Andreas Lin Sebastian Ament Maximilian Balandat E. Bakshy BDL 29 2 0 11 Oct 2024
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data Matthew Ashman Cristiana-Diana Diaconu Eric Langezaal Adrian Weller Richard E. Turner AI4TS 36 1 0 09 Oct 2024
Review of Recent Advances in Gaussian Process Regression Methods Chenyi Lyu Xingchi Liu Lyudmila Mihaylova GP 29 3 0 12 Sep 2024
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo Hester Huijsdens D. Leeftink Linda Geerligs Max Hinne 37 0 0 07 Jun 2024
Gradients of Functions of Large Matrices Nicholas Krämer Pablo Moreno-Muñoz Hrittik Roy Søren Hauberg 40 0 0 27 May 2024
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression Clara Menzen Marnix Fetter Manon Kok 22 1 0 25 Oct 2023
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models Frederiek Wesel Kim Batselier 19 1 0 11 Sep 2023
Unveiling the frontiers of deep learning: innovations shaping diverse domains Shams Forruque Ahmed Md. Sakib Bin Alam Maliha Kabir Shaila Afrin Sabiha Jannat Rafa Aanushka Mehjabin Amir H. Gandomi AI4CE 42 2 0 06 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 30 6 0 25 Jul 2023
Privacy-aware Gaussian Process Regression Rui Tuo R. Bhattacharya 8 1 0 25 May 2023
Kernel Interpolation with Sparse Grids Mohit Yadav Daniel Sheldon Cameron Musco 15 4 0 23 May 2023
Uniform approximation of common Gaussian process kernels using equispaced Fourier grids A. Barnett P. Greengard M. Rachh 23 7 0 18 May 2023
SKI to go Faster: Accelerating Toeplitz Neural Networks via Asymmetric Kernels Alexander Moreno Jonathan Mei Luke Walters 21 0 0 15 May 2023
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification Thomas Beckers Tom Z. Jiahao George J. Pappas 31 2 0 15 May 2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior Thomas Beckers Jacob H. Seidman P. Perdikaris George J. Pappas PINN 29 17 0 15 May 2023
Actually Sparse Variational Gaussian Processes Harry Jake Cunningham Daniel Augusto R. M. A. de Souza So Takao Mark van der Wilk M. Deisenroth 29 5 0 11 Apr 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift Miguel Rios A. Abu-Hanna OOD 22 1 0 01 Dec 2022
Counterfactual Learning with Multioutput Deep Kernels A. Caron G. Baio I. Manolopoulou BDL CML OffRL 25 1 0 20 Nov 2022
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions Frida Viset R. Helmons Manon Kok 34 1 0 17 Oct 2022
Optimal Sensor Placement in Body Surface Networks using Gaussian Processes Emad Alenany Changqing Cheng 11 0 0 07 Sep 2022
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data Mengying Lei A. Labbe Lijun Sun 20 1 0 21 Aug 2022
Integrating Random Effects in Deep Neural Networks Giora Simchoni Saharon Rosset BDL AI4CE 35 21 0 07 Jun 2022
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates A. Maraval Matthieu Zimmer Antoine Grosnit Rasul Tutunov Jun Wang H. Ammar 30 2 0 27 May 2022
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation Alec M. Dunton Benjamin W. Priest Amanda Muyskens GP 32 3 0 22 May 2022
Efficient Learning of Inverse Dynamics Models for Adaptive Computed Torque Control David Jorge Gabriella Pizzuto M. Mistry 11 3 0 10 May 2022
Adaptive Cholesky Gaussian Processes Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 33 3 0 22 Feb 2022
Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics J. Ryan Anil Damle 16 0 0 08 Feb 2022
Giga-scale Kernel Matrix Vector Multiplication on GPU Robert Hu Siu Lun Chau Dino Sejdinovic J. Glaunès 29 2 0 02 Feb 2022
GPEX, A Framework For Interpreting Artificial Neural Networks Amir Akbarnejad G. Bigras Nilanjan Ray 44 4 0 18 Dec 2021
A Bayesian take on option pricing with Gaussian processes Martin Tegnér Stephen J. Roberts GP 6 2 0 07 Dec 2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 20 3 0 19 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 19 31 0 02 Nov 2021
GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps Riccardo Giubilato Cedric Le Gentil Mallikarjuna Vayugundla M. J. Schuster Teresa Vidal-Calleja Rudolph Triebel 21 9 0 14 Sep 2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference Misha Padidar Xinran Zhu Leo Huang Jacob R. Gardner D. Bindel BDL 14 18 0 08 Jul 2021
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger Geoff Pleiss Philipp Hennig John P. Cunningham Jacob R. Gardner 22 24 0 01 Jul 2021
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors Liwei Wang Suraj Yerramilli Akshay Iyer D. Apley Ping Zhu Wei Chen 38 25 0 26 Jun 2021
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes Will Tebbutt Arno Solin Richard Turner 21 8 0 18 Jun 2021
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks J. Fuhg M. Marino N. Bouklas 31 59 0 07 May 2021
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey Kai Chen Qinglei Kong Yijue Dai Yue Xu Feng Yin Lexi Xu Shuguang Cui 33 30 0 18 Mar 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 17 8 0 28 Feb 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
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games Roxana Rădulescu T. Verstraeten Yijie Zhang Patrick Mannion D. Roijers A. Nowé 25 14 0 14 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 57 0 08 Nov 2020
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting Kai Chen Twan van Laarhoven E. Marchiori AI4TS 39 8 0 08 Nov 2020