James Hensman

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 53

Top Topics

Gaussian Process

Top Venues

Publications

Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. CoRR (2024)
Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari Do Nascimento, Torsten Hoefler, James Hensman
SliceGPT: Compress Large Language Models by Deleting Rows and Columns. ICLR (2024)
Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari Do Nascimento, Torsten Hoefler, James Hensman
SliceGPT: Compress Large Language Models by Deleting Rows and Columns. CoRR (2024)
Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra
Structured Entity Extraction Using Large Language Models. CoRR (2024)
Stefanos Eleftheriadis, Dominic Richards, James Hensman
Sparse Gaussian Processes with Spherical Harmonic Features Revisited. CoRR (2023)
Ouail Kitouni, Niklas Nolte, James Hensman, Bhaskar Mitra
KBFormer: A Diffusion Model for Structured Entity Completion. CoRR (2023)
Xiaoyu Lu, Alexis Boukouvalas, James Hensman
Additive Gaussian Processes Revisited. CoRR (2022)
Xiaoyu Lu, Alexis Boukouvalas, James Hensman
Additive Gaussian Processes Revisited. ICML (2022)
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR (2021)
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John
GPflux: A Library for Deep Gaussian Processes. CoRR (2021)
Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS (2021)
Nuha Bintayyash, Sokratia Georgaka, S. T. John, Sumon Ahmed, Alexis Boukouvalas, James Hensman, Magnus Rattray
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioinform. 37 (21) (2021)
Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
Amortized variance reduction for doubly stochastic objectives. CoRR (2020)
Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman
Doubly Sparse Variational Gaussian Processes. AISTATS (2020)
Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman
Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS (2020)
Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman
Doubly Sparse Variational Gaussian Processes. CoRR (2020)
Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
Amortized variance reduction for doubly stochastic objective. UAI (2020)
Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR (2020)
Vincent Dutordoir, Nicolas Durrande, James Hensman
Sparse Gaussian Processes with Spherical Harmonic Features. CoRR (2020)
Vincent Dutordoir, Nicolas Durrande, James Hensman
Sparse Gaussian Processes with Spherical Harmonic Features. ICML (2020)
Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth
Deep Gaussian Processes with Importance-Weighted Variational Inference. ICML (2019)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML (2019)
Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. AISTATS (2019)
Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman
Pseudo-Extended Markov chain Monte Carlo. NeurIPS (2019)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR (2019)
Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. CoRR (2019)
Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman
Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR (2019)
Mark van der Wilk, S. T. John, Artem Artemev, James Hensman
Variational Gaussian Process Models without Matrix Inverses. AABI (2019)
Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth
Deep Gaussian Processes with Importance-Weighted Variational Inference. CoRR (2019)
Hossein Soleimani, James Hensman, Suchi Saria
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. IEEE Trans. Pattern Anal. Mach. Intell. 40 (8) (2018)
Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth
Gaussian Process Conditional Density Estimation. NeurIPS (2018)
Arno Solin, James Hensman, Richard E. Turner
Infinite-Horizon Gaussian Processes. NeurIPS (2018)
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. CoRR (2018)
Arno Solin, James Hensman, Richard E. Turner
Infinite-Horizon Gaussian Processes. CoRR (2018)
Vincent Dutordoir, Hugh Salimbeni, Marc Peter Deisenroth, James Hensman
Gaussian Process Conditional Density Estimation. CoRR (2018)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Non-Factorised Variational Inference in Dynamical Systems. CoRR (2018)
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. AISTATS (2018)
Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
Learning Invariances using the Marginal Likelihood. NeurIPS (2018)
S. T. John, James Hensman
Large-Scale Cox Process Inference using Variational Fourier Features. CoRR (2018)
Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman
Learning Invariances using the Marginal Likelihood. CoRR (2018)
S. T. John, James Hensman
Large-Scale Cox Process Inference using Variational Fourier Features. ICML (2018)
Hossein Soleimani, James Hensman, Suchi Saria
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. CoRR (2017)
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Convolutional Gaussian Processes. NIPS (2017)
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman
GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn. Res. 18 (2017)
James Hensman, Nicolas Durrande, Arno Solin
Variational Fourier Features for Gaussian Processes. J. Mach. Learn. Res. 18 (2017)
Stefanos Eleftheriadis, Tom Nicholson, Marc Peter Deisenroth, James Hensman
Identification of Gaussian Process State Space Models. NIPS (2017)
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Convolutional Gaussian Processes. CoRR (2017)
Vincent Adam, James Hensman, Maneesh Sahani
Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. MLSP (2016)
Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
Detecting periodicities with Gaussian processes. PeerJ Comput. Sci. 2 (2016)
Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. AISTATS (2016)
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
Chained Gaussian Processes. AISTATS (2016)
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence
Chained Gaussian Processes. CoRR (2016)
Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
Detecting periodicities with Gaussian processes. PeerJ Prepr. 4 (2016)
James Hensman, Panagiotis Papastamoulis, Peter Glaus, Antti Honkela, Magnus Rattray
Fast and accurate approximate inference of transcript expression from RNA-seq data. Bioinform. 31 (24) (2015)
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani
MCMC for Variationally Sparse Gaussian Processes. NIPS (2015)
James Hensman, Magnus Rattray, Neil D. Lawrence
Fast Nonparametric Clustering of Structured Time-Series. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Zhenwen Dai, James Hensman, Neil D. Lawrence
Spike and Slab Gaussian Process Latent Variable Models. CoRR (2015)
James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani
Scalable Variational Gaussian Process Classification. AISTATS (2015)
James Hensman, Max Zwiessele, Neil D. Lawrence
Tilted Variational Bayes. AISTATS (2014)
Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence
Gaussian Process Models with Parallelization and GPU acceleration. CoRR (2014)
Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence
Hybrid Discriminative-Generative Approach with Gaussian Processes. AISTATS (2014)
James Hensman, Magnus Rattray, Neil D. Lawrence
Fast variational inference for nonparametric clustering of structured time-series. CoRR (2014)
James Hensman, Nicoló Fusi, Neil D. Lawrence
Gaussian Processes for Big Data. CoRR (2013)
James Hensman, Nicoló Fusi, Neil D. Lawrence
Gaussian Processes for Big Data. UAI (2013)
James Hensman, Neil D. Lawrence, Magnus Rattray
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinform. 14 (2013)
James Hensman, Magnus Rattray, Neil D. Lawrence
Fast Variational Inference in the Conjugate Exponential Family CoRR (2012)
James Hensman, Magnus Rattray, Neil D. Lawrence
Fast Variational Inference in the Conjugate Exponential Family. NIPS (2012)