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2020 – today
- 2024
[c29]Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra:
Learning to Extract Structured Entities Using Language Models. EMNLP 2024: 6817-6834- [c28]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 - [i33]Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari Do Nascimento, Torsten Hoefler, James Hensman:
SliceGPT: Compress Large Language Models by Deleting Rows and Columns. CoRR abs/2401.15024 (2024) - [i32]Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra:
Structured Entity Extraction Using Large Language Models. CoRR abs/2402.04437 (2024) - [i31]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 abs/2404.00456 (2024) - [i30]Xi Wang, Liana Mikaelyan, Taketomo Isazawa, James Hensman:
KBLaM: Knowledge Base augmented Language Model. CoRR abs/2410.10450 (2024) - [i29]Tycho F. A. van der Ouderaa, Maximilian L. Croci, Agrin Hilmkil, James Hensman:
Pyramid Vector Quantization for LLMs. CoRR abs/2410.16926 (2024) - 2023
- [i28]Stefanos Eleftheriadis, Dominic Richards, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features Revisited. CoRR abs/2303.15948 (2023) - [i27]Ouail Kitouni, Niklas Nolte, James Hensman, Bhaskar Mitra:
KBFormer: A Diffusion Model for Structured Entity Completion. CoRR abs/2312.05253 (2023) - 2022
- [c27]Xiaoyu Lu, Alexis Boukouvalas, James Hensman:
Additive Gaussian Processes Revisited. ICML 2022: 14358-14383 - [i26]Xiaoyu Lu, Alexis Boukouvalas, James Hensman:
Additive Gaussian Processes Revisited. CoRR abs/2206.09861 (2022) - 2021
- [j8]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): 3788-3795 (2021) - [c26]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: 9443-9455 - [i25]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 abs/2104.05674 (2021) - [i24]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 abs/2105.04504 (2021) - 2020
- [c25]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman:
Bayesian Image Classification with Deep Convolutional Gaussian Processes. AISTATS 2020: 1529-1539 - [c24]Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman:
Doubly Sparse Variational Gaussian Processes. AISTATS 2020: 2874-2884 - [c23]Vincent Dutordoir, Nicolas Durrande, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features. ICML 2020: 2793-2802 - [c22]Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objective. UAI 2020: 61-70 - [i23]Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman:
Doubly Sparse Variational Gaussian Processes. CoRR abs/2001.05363 (2020) - [i22]Mark van der Wilk, Vincent Dutordoir, S. T. John, Artem Artemev, Vincent Adam, James Hensman:
A Framework for Interdomain and Multioutput Gaussian Processes. CoRR abs/2003.01115 (2020) - [i21]Ayman Boustati, Sattar Vakili, James Hensman, S. T. John:
Amortized variance reduction for doubly stochastic objectives. CoRR abs/2003.04125 (2020) - [i20]Vincent Dutordoir, Nicolas Durrande, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features. CoRR abs/2006.16649 (2020)
2010 – 2019
- 2019
- [c21]Mark van der Wilk, S. T. John, Artem Artemev, James Hensman:
Variational Gaussian Process Models without Matrix Inverses. AABI 2019: 1-9 - [c20]Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman:
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. AISTATS 2019: 2780-2789 - [c19]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML 2019: 2931-2940 - [c18]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. ICML 2019: 5589-5598 - [c17]Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman:
Pseudo-Extended Markov chain Monte Carlo. NeurIPS 2019: 4314-4324 - [i19]Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman:
Translation Insensitivity for Deep Convolutional Gaussian Processes. CoRR abs/1902.05888 (2019) - [i18]Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman:
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. CoRR abs/1902.10078 (2019) - [i17]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. CoRR abs/1905.05435 (2019) - [i16]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR abs/1906.05828 (2019) - 2018
- [j7]Hossein Soleimani, James Hensman, Suchi Saria:
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. IEEE Trans. Pattern Anal. Mach. Intell. 40(8): 1948-1963 (2018) - [c16]Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman:
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. AISTATS 2018: 689-697 - [c15]S. T. John, James Hensman:
Large-Scale Cox Process Inference using Variational Fourier Features. ICML 2018: 2367-2375 - [c14]Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth:
Gaussian Process Conditional Density Estimation. NeurIPS 2018: 2391-2401 - [c13]Arno Solin, James Hensman, Richard E. Turner:
Infinite-Horizon Gaussian Processes. NeurIPS 2018: 3490-3499 - [c12]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. NeurIPS 2018: 9960-9970 - [i15]Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman:
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. CoRR abs/1803.09151 (2018) - [i14]S. T. John, James Hensman:
Large-Scale Cox Process Inference using Variational Fourier Features. CoRR abs/1804.01016 (2018) - [i13]Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman:
Learning Invariances using the Marginal Likelihood. CoRR abs/1808.05563 (2018) - [i12]Vincent Dutordoir, Hugh Salimbeni, Marc Peter Deisenroth, James Hensman:
Gaussian Process Conditional Density Estimation. CoRR abs/1810.12750 (2018) - [i11]Arno Solin, James Hensman, Richard E. Turner:
Infinite-Horizon Gaussian Processes. CoRR abs/1811.06588 (2018) - [i10]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Non-Factorised Variational Inference in Dynamical Systems. CoRR abs/1812.06067 (2018) - 2017
- [j6]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: 40:1-40:6 (2017) - [j5]James Hensman, Nicolas Durrande, Arno Solin:
Variational Fourier Features for Gaussian Processes. J. Mach. Learn. Res. 18: 151:1-151:52 (2017) - [c11]Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. NIPS 2017: 2849-2858 - [c10]Stefanos Eleftheriadis, Tom Nicholson, Marc Peter Deisenroth, James Hensman:
Identification of Gaussian Process State Space Models. NIPS 2017: 5309-5319 - [i9]Hossein Soleimani, James Hensman, Suchi Saria:
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction. CoRR abs/1708.04757 (2017) - [i8]Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. CoRR abs/1709.01894 (2017) - 2016
- [j4]Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence:
Detecting periodicities with Gaussian processes. PeerJ Comput. Sci. 2: e50 (2016) - [c9]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: 231-239 - [c8]Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. AISTATS 2016: 1431-1440 - [c7]Vincent Adam, James Hensman, Maneesh Sahani:
Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. MLSP 2016: 1-6 - [i7]Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence:
Chained Gaussian Processes. CoRR abs/1604.05263 (2016) - [i6]Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence:
Detecting periodicities with Gaussian processes. PeerJ Prepr. 4: e1743 (2016) - 2015
- [j3]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): 3881-3889 (2015) - [j2]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Nonparametric Clustering of Structured Time-Series. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 383-393 (2015) - [c6]James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani:
Scalable Variational Gaussian Process Classification. AISTATS 2015 - [c5]James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani:
MCMC for Variationally Sparse Gaussian Processes. NIPS 2015: 1648-1656 - [i5]Zhenwen Dai, James Hensman, Neil D. Lawrence:
Spike and Slab Gaussian Process Latent Variable Models. CoRR abs/1505.02434 (2015) - 2014
- [c4]Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence:
Hybrid Discriminative-Generative Approach with Gaussian Processes. AISTATS 2014: 47-56 - [c3]James Hensman, Max Zwiessele, Neil D. Lawrence:
Tilted Variational Bayes. AISTATS 2014: 356-364 - [i4]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast variational inference for nonparametric clustering of structured time-series. CoRR abs/1401.1605 (2014) - [i3]Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence:
Gaussian Process Models with Parallelization and GPU acceleration. CoRR abs/1410.4984 (2014) - 2013
- [j1]James Hensman, Neil D. Lawrence, Magnus Rattray:
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinform. 14: 252 (2013) - [c2]James Hensman, Nicoló Fusi, Neil D. Lawrence:
Gaussian Processes for Big Data. UAI 2013 - [i2]James Hensman, Nicoló Fusi, Neil D. Lawrence:
Gaussian Processes for Big Data. CoRR abs/1309.6835 (2013) - 2012
- [c1]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Variational Inference in the Conjugate Exponential Family. NIPS 2012: 2897-2905 - [i1]James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Variational Inference in the Conjugate Exponential Family. CoRR abs/1206.5162 (2012)
Coauthor Index
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