James Hensman

According to our database, James Hensman authored at least 53 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs.

Hao Kang, Srikant Bharadwaj, James Hensman, Tushar Krishna, Victor Rühle, Saravan Rajmohan

CoRR, 2024

Low-Rank Correction for Quantized LLMs.

Meyer Scetbon, James Hensman

CoRR, 2024

Pyramid Vector Quantization for LLMs.

Tycho F. A. van der Ouderaa, Maximilian L. Croci, Agrin Hilmkil, James Hensman

CoRR, 2024

KBLaM: Knowledge Base augmented Language Model.

Xi Wang, Liana Mikaelyan, Taketomo Isazawa, James Hensman

CoRR, 2024

QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman

CoRR, 2024

Structured Entity Extraction Using Large Language Models.

Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra

CoRR, 2024

SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari Do Nascimento, Torsten Hoefler, James Hensman

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Learning to Extract Structured Entities Using Language Models.

Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

KBFormer: A Diffusion Model for Structured Entity Completion.

Ouail Kitouni, Niklas Nolte, James Hensman, Bhaskar Mitra

CoRR, 2023

Sparse Gaussian Processes with Spherical Harmonic Features Revisited.

Stefanos Eleftheriadis, Dominic Richards, James Hensman

CoRR, 2023

2022

Additive Gaussian Processes Revisited.

Xiaoyu Lu, Alexis Boukouvalas, James Hensman

Proceedings of the International Conference on Machine Learning, 2022

2021

GPflux: A Library for Deep Gaussian Processes.

Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John

CoRR, 2021

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.

Nuha Bintayyash, Sokratia Georgaka, S. T. John, Sumon Ahmed, Alexis Boukouvalas, James Hensman, Magnus Rattray

Bioinform., 2021

Deep Neural Networks as Point Estimates for Deep Gaussian Processes.

Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Amortized variance reduction for doubly stochastic objectives.

Ayman Boustati, Sattar Vakili, James Hensman, S. T. John

CoRR, 2020

A Framework for Interdomain and Multioutput Gaussian Processes.

Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman

CoRR, 2020

Amortized variance reduction for doubly stochastic objective.

Ayman Boustati, Sattar Vakili, James Hensman, S. T. John

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Sparse Gaussian Processes with Spherical Harmonic Features.

Vincent Dutordoir, Nicolas Durrande, James Hensman

Proceedings of the 37th International Conference on Machine Learning, 2020

Bayesian Image Classification with Deep Convolutional Gaussian Processes.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Doubly Sparse Variational Gaussian Processes.

Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Translation Insensitivity for Deep Convolutional Gaussian Processes.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman

CoRR, 2019

Pseudo-Extended Markov chain Monte Carlo.

Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Gaussian Processes with Importance-Weighted Variational Inference.

Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth

Proceedings of the 36th International Conference on Machine Learning, 2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models.

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

Proceedings of the 36th International Conference on Machine Learning, 2019

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era.

Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Variational Gaussian Process Models without Matrix Inverses.

Mark van der Wilk, S. T. John, Artem Artemev, James Hensman

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

Hossein Soleimani, James Hensman, Suchi Saria

IEEE Trans. Pattern Anal. Mach. Intell., 2018

Non-Factorised Variational Inference in Dynamical Systems.

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

CoRR, 2018

Learning Invariances using the Marginal Likelihood.

Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Infinite-Horizon Gaussian Processes.

Arno Solin, James Hensman, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Gaussian Process Conditional Density Estimation.

Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Large-Scale Cox Process Inference using Variational Fourier Features.

S. T. John, James Hensman

Proceedings of the 35th International Conference on Machine Learning, 2018

Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models.

Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

GPflow: A Gaussian Process Library using TensorFlow.

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman

J. Mach. Learn. Res., 2017

Variational Fourier Features for Gaussian Processes.

James Hensman, Nicolas Durrande, Arno Solin

J. Mach. Learn. Res., 2017

Convolutional Gaussian Processes.

Mark van der Wilk, Carl Edward Rasmussen, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Identification of Gaussian Process State Space Models.

Stefanos Eleftheriadis, Tom Nicholson, Marc Peter Deisenroth, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Detecting periodicities with Gaussian processes.

Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence

PeerJ Prepr., 2016

Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference.

Vincent Adam, James Hensman, Maneesh Sahani

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

Chained Gaussian Processes.

Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes.

Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Fast Nonparametric Clustering of Structured Time-Series.

James Hensman, Magnus Rattray, Neil D. Lawrence

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Spike and Slab Gaussian Process Latent Variable Models.

Zhenwen Dai, James Hensman, Neil D. Lawrence

CoRR, 2015

Fast and accurate approximate inference of transcript expression from RNA-seq data.

James Hensman, Panagiotis Papastamoulis, Peter Glaus, Antti Honkela, Magnus Rattray

Bioinform., 2015

MCMC for Variationally Sparse Gaussian Processes.

James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Scalable Variational Gaussian Process Classification.

James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Fast variational inference for nonparametric clustering of structured time-series.

James Hensman, Magnus Rattray, Neil D. Lawrence

CoRR, 2014

Gaussian Process Models with Parallelization and GPU acceleration.

Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence

CoRR, 2014

Hybrid Discriminative-Generative Approach with Gaussian Processes.

Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Tilted Variational Bayes.

James Hensman, Max Zwiessele, Neil D. Lawrence

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

James Hensman, Neil D. Lawrence, Magnus Rattray

BMC Bioinform., 2013

Gaussian Processes for Big Data.

James Hensman, Nicoló Fusi, Neil D. Lawrence

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012

Fast Variational Inference in the Conjugate Exponential Family.

James Hensman, Magnus Rattray, Neil D. Lawrence

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012