← Authors

James Hensman

30 papers · 2012–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (11) 🌈 Renaissance Researcher (6) πŸƒ Academic Marathon (13) 🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) πŸ”¬ Deep Specialist (19) πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (99) πŸ’Ž Century Club (30) πŸ”₯ Unstoppable (9) ⚑ Prolific Year (6)

Conferences

NIPS (10) AISTATS (9) ICML (5) ICLR (2) JMLR (2) EMNLP (1) UAI (1)

Top co-authors

Nicolas Durrande (5) Vincent Dutordoir (5) Zoubin Ghahramani (5) Mark van der Wilk (5) Stefanos Eleftheriadis (4) Neil D. Lawrence (4) Marc Deisenroth (3) ST John (3) Hugh Salimbeni (3) Torsten Hoefler (2)

Keywords

variational inference (21) gaussian process (19) sparse approximation (6) marginal likelihood (5) inducing point (4) approximate inference (3) bayesian inference (3) automatic differentiation (3) deep gaussian process (3) posterior approximation (3) gaussian processes (2) additive model (2) fourier feature (2) hyperparameter learning (2) convolutional kernel (2) stochastic optimization (2) expectation propagation (2) image classification (2) uncertainty quantification (2) markov chain monte carlo (2)

Papers

KBLaM: Knowledge Base augmented Language Model ICLR 2025
Learning to Extract Structured Entities Using Language Models EMNLP 2024
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs NIPS 2024
SliceGPT: Compress Large Language Models by Deleting Rows and Columns ICLR 2024
Additive Gaussian Processes Revisited ICML 2022
Deep Neural Networks as Point Estimates for Deep Gaussian Processes NIPS 2021
Bayesian Image Classification with Deep Convolutional Gaussian Processes AISTATS 2020
Sparse Gaussian Processes with Spherical Harmonic Features ICML 2020
Doubly Sparse Variational Gaussian Processes AISTATS 2020
Amortized variance reduction for doubly stochastic objective UAI 2020
Deep Gaussian Processes with Importance-Weighted Variational Inference ICML 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019
Pseudo-Extended Markov chain Monte Carlo NIPS 2019
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era AISTATS 2019
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models AISTATS 2018
Gaussian Process Conditional Density Estimation NIPS 2018
Infinite-Horizon Gaussian Processes NIPS 2018
Learning Invariances using the Marginal Likelihood NIPS 2018
Large-Scale Cox Process Inference using Variational Fourier Features ICML 2018
Variational Fourier Features for Gaussian Processes JMLR 2018
Convolutional Gaussian Processes NIPS 2017
GPflow: A Gaussian Process Library using TensorFlow JMLR 2017
Identification of Gaussian Process State Space Models NIPS 2017
Chained Gaussian Processes AISTATS 2016
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes AISTATS 2016
MCMC for Variationally Sparse Gaussian Processes NIPS 2015
Scalable Variational Gaussian Process Classification AISTATS 2015
Tilted Variational Bayes AISTATS 2014
Hybrid Discriminative-Generative Approach with Gaussian Processes AISTATS 2014
Fast Variational Inference in the Conjugate Exponential Family NIPS 2012