← Authors

Carl Edward Rasmussen

24 papers · 2005–2024 · 6 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (19) 🐝 Cross-Pollinator (15) 🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (3) πŸ”¬ Deep Specialist (11) πŸ’Ž Century Club (24) πŸ“ˆ Trend Setter ❓ The Questioner πŸ”₯ Unstoppable (7) πŸ—ƒοΈ Keyword Collector (84)

Conferences

NIPS (10) JMLR (8) ICML (3) AISTATS (1) ICLR (1) NAACL (1)

Top co-authors

Mark van der Wilk (7) Vidhi Lalchand (3) David R. Burt (2) David Burt (2) Hannes Nickisch (2) Roger Frigola (2) Fergus Simpson (2) James Hensman (2) Malte Kuss (1) Robert Peharz (1)

Keywords

gaussian process (17) variational inference (10) sparse approximation (4) markov chain monte carlo (3) approximate inference (3) sparse gaussian process (3) expectation propagation (3) bayesian inference (3) dynamical system (3) inducing variable (2) state-space model (2) particle filter (2) marginal likelihood (2) gaussian process regression (2) model-based reinforcement learning (2) inducing point (2) kl divergence (1) uncertainty quantification (1) probabilistic modeling (1) hyperparameter optimization (1)

Papers

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees JMLR 2024
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NIPS 2022
Kernel Identification Through Transformers NIPS 2021
Clipping Loops for Sample-Efficient Dialogue Policy Optimisation NAACL 2021
Marginalised Gaussian Processes with Nested Sampling NIPS 2021
Deep Structured Mixtures of Gaussian Processes AISTATS 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression JMLR 2020
Ensembling geophysical models with Bayesian Neural Networks NIPS 2020
Rates of Convergence for Sparse Variational Gaussian Process Regression ICML 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019
Deep Convolutional Networks as shallow Gaussian Processes ICLR 2019
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos ICML 2018
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs NIPS 2017
Convolutional Gaussian Processes NIPS 2017
Understanding Probabilistic Sparse Gaussian Process Approximations NIPS 2016
Variational Gaussian Process State-Space Models NIPS 2014
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models NIPS 2014
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC NIPS 2013
Sparse Spectrum Gaussian Process Regression JMLR 2010
Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010
Approximations for Binary Gaussian Process Classification JMLR 2008
The Need for Open Source Software in Machine Learning JMLR 2007
A Unifying View of Sparse Approximate Gaussian Process Regression JMLR 2005
Assessing Approximate Inference for Binary Gaussian Process Classification JMLR 2005