Carl Edward Rasmussen

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

Achievements

+10 more ↓

🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (5) 🗺️ Taxonomy Completionist (12) 🐣 Hot Topic Early Bird

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (3) 🔬 Deep Specialist (11) 💎 Century Club (24) 📈 Trend Setter ❓ The Questioner 🔥 Unstoppable (7) 🗃️ 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