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← Authors

James Hensman

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

Achievements

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+9 more ↓ 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (11) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7)
🐣 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