Fast Variational Inference in the Conjugate Exponential Family. - James Hensman; Magnus Rattray; Neil D. Lawrence (NIPS, 2012)

Gaussian Processes for Big Data. - James Hensman; Nicoló Fusi; Neil D. Lawrence (UAI, 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)

Tilted Variational Bayes. - James Hensman; Max Zwiessele; Neil D. Lawrence (AISTATS, 2014)

Hybrid Discriminative-Generative Approach with Gaussian Processes. - Ricardo Andrade Pacheco; James Hensman; Max Zwiessele; Neil D. Lawrence (AISTATS, 2014)

Gaussian Process Models with Parallelization and GPU acceleration. - Zhenwen Dai; Andreas C. Damianou; James Hensman; Neil D. Lawrence (CoRR, 2014)

Fast variational inference for nonparametric clustering of structured time-series. - James Hensman; Magnus Rattray; Neil D. Lawrence (CoRR, 2014)

Scalable Variational Gaussian Process Classification. - James Hensman; Alexander G. de G. Matthews; Zoubin Ghahramani (AISTATS, 2015)

MCMC for Variationally Sparse Gaussian Processes. - James Hensman; Alexander G. de G. Matthews; Maurizio Filippone; Zoubin Ghahramani (NIPS, 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)

Spike and Slab Gaussian Process Latent Variable Models. - Zhenwen Dai; James Hensman; Neil D. Lawrence (CoRR, 2015)

Fast Nonparametric Clustering of Structured Time-Series. - James Hensman; Magnus Rattray; Neil D. Lawrence (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. - Alexander G. de G. Matthews; James Hensman; Richard E. Turner; Zoubin Ghahramani (AISTATS, 2016)

Chained Gaussian Processes. - Alan D. Saul; James Hensman; Aki Vehtari; Neil D. Lawrence (AISTATS, 2016)

Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. - Vincent Adam; James Hensman; Maneesh Sahani (MLSP, 2016)

Detecting periodicities with Gaussian processes. - Nicolas Durrande; James Hensman; Magnus Rattray; Neil D. Lawrence (PeerJ Prepr., 2016)

Identification of Gaussian Process State Space Models. - Stefanos Eleftheriadis; Tom Nicholson; Marc Peter Deisenroth; James Hensman (NIPS, 2017)

Convolutional Gaussian Processes. - Mark van der Wilk; Carl Edward Rasmussen; James Hensman (NIPS, 2017)

Variational Fourier Features for Gaussian Processes. - James Hensman; Nicolas Durrande; Arno Solin (J. Mach. Learn. Res., 2017)

GPflow: A Gaussian Process Library using TensorFlow. - Alexander G. de G. Matthews; Mark van der Wilk; Tom Nickson; Keisuke Fujii 002; Alexis Boukouvalas; Pablo León-Villagrá; Zoubin Ghahramani; James Hensman (J. Mach. Learn. Res., 2017)

Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. - Hugh Salimbeni; Stefanos Eleftheriadis; James Hensman (AISTATS, 2018)

Large-Scale Cox Process Inference using Variational Fourier Features. - S. T. John; James Hensman (ICML, 2018)

Gaussian Process Conditional Density Estimation. - Vincent Dutordoir; Hugh Salimbeni; James Hensman; Marc Peter Deisenroth (NeurIPS, 2018)

Infinite-Horizon Gaussian Processes. - Arno Solin; James Hensman; Richard E. Turner (NeurIPS, 2018)

Learning Invariances using the Marginal Likelihood. - Mark van der Wilk; Matthias Bauer 001; S. T. John; James Hensman (NeurIPS, 2018)

Non-Factorised Variational Inference in Dynamical Systems. - Alessandro Davide Ialongo; Mark van der Wilk; James Hensman; Carl Edward Rasmussen (CoRR, 2018)

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. - Hossein Soleimani; James Hensman; Suchi Saria (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Variational Gaussian Process Models without Matrix Inverses. - Mark van der Wilk; S. T. John; Artem Artemev; James Hensman (AABI, 2019)

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. - Nicolas Durrande; Vincent Adam; Lucas Bordeaux; Stefanos Eleftheriadis; James Hensman (AISTATS, 2019)

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. - Alessandro Davide Ialongo; Mark van der Wilk; James Hensman; Carl Edward Rasmussen (ICML, 2019)

Deep Gaussian Processes with Importance-Weighted Variational Inference. - Hugh Salimbeni; Vincent Dutordoir; James Hensman; Marc Peter Deisenroth (ICML, 2019)

Pseudo-Extended Markov chain Monte Carlo. - Christopher Nemeth; Fredrik Lindsten; Maurizio Filippone; James Hensman (NeurIPS, 2019)

Translation Insensitivity for Deep Convolutional Gaussian Processes. - Vincent Dutordoir; Mark van der Wilk; Artem Artemev; Marcin Tomczak; James Hensman (CoRR, 2019)

Doubly Sparse Variational Gaussian Processes. - Vincent Adam; Stefanos Eleftheriadis; Artem Artemev; Nicolas Durrande; James Hensman (AISTATS, 2020)

Bayesian Image Classification with Deep Convolutional Gaussian Processes. - Vincent Dutordoir; Mark van der Wilk; Artem Artemev; James Hensman (AISTATS, 2020)

Sparse Gaussian Processes with Spherical Harmonic Features. - Vincent Dutordoir; Nicolas Durrande; James Hensman (ICML, 2020)

Amortized variance reduction for doubly stochastic objective. - Ayman Boustati; Sattar Vakili; James Hensman; S. T. John (UAI, 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 objectives. - Ayman Boustati; Sattar Vakili; James Hensman; S. T. John (CoRR, 2020)

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 (NeurIPS, 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)

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)

Additive Gaussian Processes Revisited. - Xiaoyu Lu; Alexis Boukouvalas; James Hensman (ICML, 2022)

Sparse Gaussian Processes with Spherical Harmonic Features Revisited. - Stefanos Eleftheriadis; Dominic Richards; James Hensman (CoRR, 2023)

KBFormer: A Diffusion Model for Structured Entity Completion. - Ouail Kitouni; Niklas Nolte; James Hensman; Bhaskar Mitra 001 (CoRR, 2023)

Learning to Extract Structured Entities Using Language Models. - Haolun Wu; Ye Yuan 017; Liana Mikaelyan; Alexander Meulemans; Xue Liu 004; James Hensman; Bhaskar Mitra 001 (EMNLP, 2024)

SliceGPT: Compress Large Language Models by Deleting Rows and Columns. - Saleh Ashkboos; Maximilian L. Croci; Marcelo Gennari Do Nascimento; Torsten Hoefler; James Hensman (ICLR, 2024)

QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. - Saleh Ashkboos; Amirkeivan Mohtashami; Maximilian L. Croci; Bo Li; Pashmina Cameron; Martin Jaggi; Dan Alistarh; Torsten Hoefler; James Hensman (NeurIPS, 2024)

Structured Entity Extraction Using Large Language Models. - Haolun Wu; Ye Yuan 017; Liana Mikaelyan; Alexander Meulemans; Xue Liu 001; James Hensman; Bhaskar Mitra 001 (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)

Pyramid Vector Quantization for LLMs. - Tycho F. A. van der Ouderaa; Maximilian L. Croci; Agrin Hilmkil; James Hensman (CoRR, 2024)

Low-Rank Correction for Quantized LLMs. - Meyer Scetbon; James Hensman (CoRR, 2024)

TurboAttention: Efficient Attention Approximation For High Throughputs LLMs. - Hao Kang; Srikant Bharadwaj; James Hensman; Tushar Krishna; Victor Rühle; Saravan Rajmohan (CoRR, 2024)

KBLaM: Knowledge Base augmented Language Model. - Xi Wang; Taketomo Isazawa; Liana Mikaelyan; James Hensman (ICLR, 2025)