On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

27 April 2015

Papers citing "On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes"

50 / 116 papers shown

Title
Functional Stochastic Gradient MCMC for Bayesian Neural Networks Mengjing Wu Junyu Xuan Jie Lu BDL 27 0 0 25 Sep 2024
Review of Recent Advances in Gaussian Process Regression Methods Chenyi Lyu Xingchi Liu Lyudmila Mihaylova GP 31 3 0 12 Sep 2024
Approximation-Aware Bayesian Optimization Natalie Maus Kyurae Kim Geoff Pleiss David Eriksson John P. Cunningham Jacob R. Gardner 31 2 0 06 Jun 2024
One-Shot Federated Learning with Bayesian Pseudocoresets Tim d'Hondt Mykola Pechenizkiy Robert Peharz FedML 37 0 0 04 Jun 2024
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes Sebastian W. Ober A. Artemev Marcel Wagenlander Rudolfs Grobins Mark van der Wilk GP 18 1 0 15 Feb 2024
A General Theory for Kernel Packets: from state space model to compactly supported basis Liang Ding Rui Tuo 13 1 0 06 Feb 2024
Continual Learning via Sequential Function-Space Variational Inference Tim G. J. Rudner Freddie Bickford-Smith Qixuan Feng Yee Whye Teh Y. Gal 30 39 0 28 Dec 2023
Tractable Function-Space Variational Inference in Bayesian Neural Networks Tim G. J. Rudner Zonghao Chen Yee Whye Teh Y. Gal 85 39 0 28 Dec 2023
Variational Gaussian Processes For Linear Inverse Problems Thibault Randrianarisoa Botond Szabó 43 3 0 01 Nov 2023
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks Balhae Kim Hyungi Lee Juho Lee BDL 29 2 0 27 Oct 2023
On permutation symmetries in Bayesian neural network posteriors: a variational perspective Simone Rossi Ankit Singh T. Hannagan 32 2 0 16 Oct 2023
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior Luke Travis Kolyan Ray 24 4 0 29 Sep 2023
Entropic Matching for Expectation Propagation of Markov Jump Processes Bastian Alt Heinz Koeppl Heinz Koeppl 34 1 0 27 Sep 2023
A Unifying Variational Framework for Gaussian Process Motion Planning Lucas Cosier Rares Iordan Sicelukwanda Zwane Giovanni Franzese James T. Wilson M. Deisenroth Alexander Terenin Yasemin Bekiroglu 3DV 24 4 0 02 Sep 2023
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes Talay M Cheema C. Rasmussen GP 33 2 0 27 Aug 2023
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation S. Bouabid Dino Sejdinovic D. Watson‐Parris 16 5 0 14 Jul 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 41 1 0 12 Jul 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent J. Lin Javier Antorán Shreyas Padhy David Janz José Miguel Hernández-Lobato Alexander Terenin 23 22 0 20 Jun 2023
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR Aneeshan Sain A. Bhunia Subhadeep Koley Pinaki Nath Chowdhury Soumitri Chattopadhyay Tao Xiang Yi-Zhe Song 28 18 0 24 Mar 2023
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not Aneeshan Sain A. Bhunia Pinaki Nath Chowdhury Subhadeep Koley Tao Xiang Yi-Zhe Song VLM 34 78 0 23 Mar 2023
On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models Tong Ye Shijing Si Jianzong Wang Ning Cheng Zhitao Li Jing Xiao 77 2 0 15 Mar 2023
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces Kalvik Jakkala Srinivas Akella 29 1 0 28 Feb 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 32 24 0 24 Jan 2023
Diffusion Generative Models in Infinite Dimensions Gavin Kerrigan Justin Ley Padhraic Smyth DiffM 50 27 0 01 Dec 2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? V. Lalchand W. Bruinsma David R. Burt C. Rasmussen GP 17 6 0 04 Nov 2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin David R. Burt A. Artemev Seth Flaxman Mark van der Wilk C. Rasmussen Hong Ge 58 7 0 14 Oct 2022
Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes Caglar Hizli S. T. John A. Juuti Tuure Saarinen Kirsi Pietiläinen Pekka Marttinen CML 14 1 0 09 Sep 2022
Variational Inference for Model-Free and Model-Based Reinforcement Learning Felix Leibfried OffRL 15 0 0 04 Sep 2022
The Neural Process Family: Survey, Applications and Perspectives Saurav Jha Dong Gong Xuesong Wang Richard Turner L. Yao BDL 78 24 0 01 Sep 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 48 6 0 15 Jun 2022
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning Veit Wild Robert Hu Dino Sejdinovic BDL 51 11 0 12 May 2022
AODisaggregation: toward global aerosol vertical profiles S. Bouabid D. Watson‐Parris Sofija Stefanović A. Nenes Dino Sejdinovic 29 0 0 06 May 2022
Modelling Non-Smooth Signals with Complex Spectral Structure W. Bruinsma Martin Tegnér Richard Turner 30 6 0 14 Mar 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning Sattar Vakili Jonathan Scarlett Da-shan Shiu A. Bernacchia 30 18 0 08 Feb 2022
Modular Gaussian Processes for Transfer Learning P. Moreno-Muñoz Antonio Artés-Rodríguez Mauricio A. Alvarez 13 4 0 26 Oct 2021
Variational Gaussian Processes: A Functional Analysis View Veit Wild George Wynne GP 43 5 0 25 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Variational Inference for Continuous-Time Switching Dynamical Systems Lukas Kohs Bastian Alt Heinz Koeppl 40 8 0 29 Sep 2021
Contraction rates for sparse variational approximations in Gaussian process regression D. Nieman Botond Szabó Harry Van Zanten 52 17 0 22 Sep 2021
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks Daniel Fernandez-Llaneza Andrea Gondova Harris Vince A. Patra M. Zurek P. Konings Patrik Kagelid L. Hultin 27 4 0 09 Sep 2021
Estimation of Riemannian distances between covariance operators and Gaussian processes H. Q. Minh 36 1 0 26 Aug 2021
Active Learning in Gaussian Process State Space Model H. Yu Dingling Yao Christoph Zimmer Marc Toussaint D. Nguyen-Tuong GP 22 4 0 30 Jul 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
Last Layer Marginal Likelihood for Invariance Learning Pola Schwobel Martin Jørgensen Sebastian W. Ober Mark van der Wilk BDL UQCV 26 28 0 14 Jun 2021
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss Dominique Heyn Jinfan Chen Andreas Krause 40 27 0 06 Jun 2021
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes Veit Wild Motonobu Kanagawa Dino Sejdinovic 38 16 0 02 Jun 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir J. Hensman Mark van der Wilk Carl Henrik Ek Zoubin Ghahramani N. Durrande BDL UQCV 18 30 0 10 May 2021
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes H. Q. Minh 24 2 0 26 Apr 2021
On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach Karla Monterrubio-Gómez S. Wade 24 2 0 04 Mar 2021
The Gaussian Neural Process W. Bruinsma James Requeima Andrew Y. K. Foong Jonathan Gordon Richard Turner BDL 25 28 0 10 Jan 2021