Marc Deisenroth
24 papers · 2010–2024 · 4 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π Conference Polyglot (4)
π§
Keyword Pioneer
π
Cross-Pollinator
(14)
π
Conference Polyglot
(4)
π
Keyword Trendsetter Combo
(3)
π
Keyword Champion
(2)
π¬
Deep Specialist
(12)
π
Trend Setter
ποΈ
Keyword Collector
(107)
π
Conference Pioneer
π₯
Unstoppable
(5)
β‘
Prolific Year
(5)
π
Century Club
(24)
Conferences
AISTATS (8)
NIPS (8)
ICML (6)
RSS (2)
Top co-authors
Research topics
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