← Authors

Marc Deisenroth

24 papers · 2010–2024 · 4 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (14) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (14) 🌍 Conference Polyglot (4) 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (12) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (107) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (5) ⚑ Prolific Year (5) πŸ’Ž Century Club (24)

Conferences

AISTATS (8) NIPS (8) ICML (6) RSS (2)

Top co-authors

Alexander Terenin (7) Hugh Salimbeni (5) Viacheslav Borovitskiy (3) James Hensman (3) Peter Mostowsky (2) James Wilson (2) Steindor Saemundsson (2) Carl Rasmussen (2) Vincent Dutordoir (2) Samuel Cohen (2)

Research topics

Robotics (2) Reinforcement Learning (1)

Keywords

gaussian process (13) variational inference (8) bayesian inference (5) uncertainty quantification (4) dynamical system (4) deep gaussian process (3) latent state (3) data-efficient reinforcement learning (2) time series (2) model-based reinforcement learning (2) imitation learning (1) expectation maximization (1) posterior sampling (1) model predictive control (1) optimal transport (1) approximate inference (1) motion planning (1) natural gradient (1) gaussian processes (1) few-shot learning (1)

Papers

A Unifying Variational Framework for Gaussian Process Motion Planning AISTATS 2024
Thin and deep Gaussian processes NIPS 2023
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NIPS 2021
MatΓ©rn Gaussian Processes on Graphs AISTATS 2021
Learning Contact Dynamics using Physically Structured Neural Networks AISTATS 2021
Aligning Time Series on Incomparable Spaces AISTATS 2021
Stochastic Differential Equations with Variational Wishart Diffusions ICML 2020
Efficiently sampling functions from Gaussian process posteriors ICML 2020
Variational Integrator Networks for Physically Structured Embeddings AISTATS 2020
Healing Products of Gaussian Process Experts ICML 2020
Deep Gaussian Processes with Importance-Weighted Variational Inference ICML 2019
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches ICML 2018
Orthogonally Decoupled Variational Gaussian Processes NIPS 2018
Gaussian Process Conditional Density Estimation NIPS 2018
Maximizing acquisition functions for Bayesian optimization NIPS 2018
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control AISTATS 2018
Identification of Gaussian Process State Space Models NIPS 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes NIPS 2017
Distributed Gaussian Processes ICML 2015
Analytic Long-Term Forecasting with Periodic Gaussian Processes AISTATS 2014
Probabilistic Modeling of Human Movements for Intention Inference RSS 2012
Expectation Propagation in Gaussian Process Dynamical Systems NIPS 2012
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning RSS 2011
State-Space Inference and Learning with Gaussian Processes AISTATS 2010