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Sicelukwanda Zwane
,
Denis Hadjivelichkov
,
Yicheng Luo
,
Yasemin Bekiroglu
,
Dimitrios Kanoulas
,
Marc P. Deisenroth
(2023).
Safe Trajectory Sampling in Model-based Reinforcement Learning
.
Proceedings of the International Conference on Automation Science and Engineering (CASE)
.
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Suman Ravuri
,
Melanie Rey
,
Shakir Mohamed
,
Marc P. Deisenroth
(2023).
Understanding Deep Generative Models with Generalized Empirical Likelihoods
.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
.
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PDF
Organizers of Queer in AI
,
Anaelia Ovalle
,
Arjun Subramonian
,
Ashwin Singh
,
Claas Voelcker
,
Danica J. Sutherland
,
Davide Locatelli
,
Eva Breznik
,
Filip Klubička
,
Hang Yuan
,
Hetvi J
,
Huan Zhang
,
Jaidev Shriram
,
Kruno Lehman
,
Luca Soldaini
,
Maarten Sap
,
Marc Peter Deisenroth
,
Maria Leonor Pacheco
,
Maria Ryskina
,
Martin Mundt
,
Melvin Selim Atay
,
Milind Agarwal
,
Nyx McLean
,
Pan Xu
,
A Pranav
,
Raj Korpan
,
Ruchira Ray
,
Sarah Mathew
,
Sarthak Arora
,
ST John
,
Tanvi Anand
,
Vishakha Agrawal
,
William Agnew
,
Yanan Long
,
Zijie J. Wang
,
Zeerak Talat
,
Avijit Ghosh
,
Nathaniel Dennler
,
Michael Noseworthy
,
Sharvani Jha
,
Emi Baylor
,
Aditya Joshi
,
Natalia Y. Bilenko
,
Andrew McNamara
,
Raphael Gontijo-Lopes
,
Alex Markham
,
Evyn Dǒng
,
Jackie Kay
,
Manu Saraswat
,
Nikhil Vytla
,
Luke Stark
(2023).
Queer In AI: A Case Study in Community-Led Participatory AI
.
Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT)
.
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URL
Yiting Chen
,
Junnan Jiang
,
Ruiqi Lei
,
Yasemin Bekiroglu
,
Fei Chen
,
Miao Li
(2023).
Deep Grasp Adaptation through Domain Transfer
.
IEEE International Conference on Robotics and Automation (ICRA)
.
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Yicheng Luo
,
Zhengyao Jiang
,
Samuel Cohen
,
Edward Grefenstette
,
Marc P. Deisenroth
(2023).
Optimal Transport for Offline Imitation Learning
.
Proceedings of the International Conference on Learning Representations (ICLR)
.
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PDF
Code
Jake Cunningham
,
Daniel de Souza
,
So Takao
,
Marc van der Wilk
,
Marc P. Deisenroth
(2023).
Actually Sparse Variational Gaussian Processes
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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PDF
Code
So Takao
,
Sean Nassimiha
,
Peter Dudfield
,
Jack Kelly
,
Marc P. Deisenroth
(2022).
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
.
NeurIPS Workshop on Tackling Climate Change with Machine Learning
.
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URL
Yicheng Luo
,
Zhengyao Jiang
,
Samuel Cohen
,
Edward Grefenstette
,
Marc P. Deisenroth
(2022).
Optimal Transport for Offline Imitation Learning
.
NeurIPS Workshop on Offline Reinforcement Learning
.
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URL
Jake Cunningham
,
So Takao
,
Mark van der Wilk
,
Marc P. Deisenroth
(2022).
Actually Sparse Variational Gaussian Processes
.
NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
.
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Denis Hadjivelichkov
,
Sicelukwanda Zwane
,
Lourdes Agapito
,
Marc P. Deisenroth
,
Dimitrios Kanoulas
(2022).
One-Shot Transfer of Affordance Regions? AffCorrs!
.
Proceedings of the Conference on Robot Learning (CoRL)
.
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Code
Zuka Murvanidze
,
Marc P. Deisenroth
,
Yasemin Bekiroglu
(2022).
Enhanced GPIS Learning Based on Local and Global Focus Areas
.
IEEE Robotics and Automation Letters
.
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Code
Michelangelo Conserva
,
Marc P. Deisenroth
,
K. S. Sesh Kumar
(2022).
The Graph Cut Kernel for Ranked Data
.
Transactions on Machine Learning Research
.
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Code
Sanket Kamthe
,
So Takao
,
Shakir Mohamed
,
Marc P. Deisenroth
(2022).
Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation
.
Transactions on Machine Learning Research
.
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Code
Linh Tran
,
Maja Pantic
,
Marc P. Deisenroth
(2022).
Cauchy-Schwarz Regularized Autoencoder
.
Journal of Machine Learning Research
.
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Daniel de Souza
,
Diego Mesquita
,
Samuel Kaski
,
Luigi Acerbi
(2022).
Parallel MCMC Without Embarrassing Failures
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Hadi Hajieghrary
,
Marc P. Deisenroth
,
Yasemin Bekiroglu
(2022).
Bayesian Optimization based Nonlinear Adaptive PID Design for Robust Control of the Joints at Mobile Manipulators
.
Proceedings of the IEEE International Conference on Automation Science and Engineering
.
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Michael J. Hutchinson
,
Alexander Terenin
,
Viacheslav Borovitskiy
,
So Takao
,
Yee Whye Teh
,
Marc P. Deisenroth
(2021).
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
.
Advances in Neural Information Processing Systems (NeurIPS)
.
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Code
Noémie Jaquier
,
Viacheslav Borovitskiy
,
Andrei Smolensky
,
Alexander Terenin
,
Tamim Asfour
,
Leonel Rozo
(2021).
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
.
Proceedings of the Conference on Robot Learning
.
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Code
Florin Gogianu
,
Tudor Berariu
,
Mihaela Rosca
,
Claudia Clopath
,
Lucian Busoniu
,
Razvan Pascanu
(2021).
Spectral Normalization for Deep Reinforcement Learning: an Optimisation Perspective
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
Samuel Cohen
,
Brandon Amos
,
Yaron Lipman
(2021).
Riemannian Convex Potential Flows
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
Mihaela Rosca
,
Yan Wu
,
Benoit Dherin
,
David Barrett
(2021).
Discretization Drift in Two-Player Games
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
James T. Wilson
,
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc P. Deisenroth
(2021).
Pathwise Conditioning of Gaussian Processes
.
Journal of Machine Learning Research
.
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Code
Nenad Tomasev
,
Kevin R. McKee
,
Jackie Kay
,
Shakir Mohamed
(2021).
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
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Jacob Menick
,
Erich Elsen
,
Utku Evci
,
Simon Osindero
,
Karen Simonyan
,
Alex Graves
(2021).
A Practical Sparse Approximation for Real Time Recurrent Learning
.
International Conference on Learning Representations (ICLR)
.
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Viacheslav Borovitskiy
,
Iskander Azangulov
,
Alexander Terenin
,
Peter Mostowsky
,
Marc Deisenroth
,
Nicolas Durrande
(2021).
Matérn Gaussian Processes on Graphs
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Code
Andreas Hochlehnert
,
Alexander Terenin
,
Steindór Sæmundsson
,
Marc Deisenroth
(2021).
Learning Contact Dynamics using Physically Structured Neural Networks
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Code
Samuel Cohen
,
Giulia Luise
,
Alexander Terenin
,
Brandon Amos
,
Marc Deisenroth
(2021).
Aligning Time Series on Incomparable Spaces
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Code
Vincent Dutordoir
,
Hugh Salimbeni
,
Eric Hambro
,
John McLeod
,
Felix Leibfried
,
Artem Artemev
,
Mark van der Wilk
,
James Hensman
,
Marc P. Deisenroth
,
ST John
(2021).
GPflux: A Library for Deep Gaussian Processes
.
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Christiana de Farias
,
Naresh Marturi
,
Rustam Stolkin
,
Yasemin Bekiroglu
(2021).
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
.
IEEE Robotics and Automation Letters
.
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Yicheng Luo
,
Antonio Filieri
,
Yuan Zhou
(2021).
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
.
Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ‘21)
.
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DOI
Jean Kaddour
,
Steindór Sæmundsson
,
Marc Deisenroth
(2020).
Probabilistic Active Meta-Learning
. Advances in Neural Information Processing Systems (NeurIPS).
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Code
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc Deisenroth
(2020).
Matérn Gaussian Processes on Riemannian Manifolds
. Advances in Neural Information Processing Systems (NeurIPS).
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Code
Alexander Terenin
,
Måns Magnusson
,
Leif Jonsson
(2020).
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
.
Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
.
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Riccardo Moriconi
,
Marc Deisenroth
,
K. S. Sesh Kumar
(2020).
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes
.
Machine Learning
.
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Code
Janith Petangoda
,
Nick A. M. Monk
,
Marc Deisenroth
(2020).
A Foliated View of Transfer Learning
. arXiv:2008.00546.
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Samuel Cohen
,
Michael Arbel
,
Marc Deisenroth
(2020).
Estimating Barycenters of Measures in High Dimensions
.
arXiv:2007.07105
.
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Martin Jørgensen
,
Marc Deisenroth
,
Hugh Salimbeni
(2020).
Stochastic Differential Equations with Variational Wishart Diffusions
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
Samuel Cohen
,
Rendani Mbuvha
,
Tshilidzi Marwala
,
Marc Deisenroth
(2020).
Healing Products of Gaussian Process Experts
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
James Wilson
,
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc Deisenroth
(2020).
Efficiently Sampling Functions from Gaussian Process Posteriors
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
Samuel Cohen
,
Giulia Luise
,
Alexander Terenin
,
Brandon Amos
,
Marc Deisenroth
(2020).
Aligning Time Series on Incomparable Spaces
.
arXiv:2006.12648
.
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Code
Steindór Sæmundsson
,
Alexander Terenin
,
Katja Hofmann
,
Marc Deisenroth
(2020).
Variational Integrator Networks for Physically Structured Embeddings
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Code
Alexander Terenin
,
Daniel Simpson
,
David Draper
(2020).
Asynchronous Gibbs Sampling
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Marc Deisenroth
,
A. Aldo Faisal
,
Cheng Soon Ong
(2020).
Mathematics for Machine Learning
.
Cambridge University Press
.
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Riccardo Moriconi
,
K. S. Sesh Kumar
,
Marc Deisenroth
(2020).
High-Dimensional Bayesian Optimization with Projections using Quantile Gaussian Processes
.
Optimization Letters
.
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DOI
Simon Olofsson
(2020).
Gaussian Processes for Hybridisation of Analytical and Data-Driven Approaches for Design of Experiments
.
PDF
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Samuel Cohen
,
Dino Sejdinovic
(2019).
On the Gromov-Wasserstein distance and Coupled Deep Generative Models
.
OTML Workshop at NeurIPS
.
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Riccardo Moriconi
,
Marc P. Deisenroth
,
K. S. Sesh Kumar
(2019).
High-Dimensional Bayesian Optimization Using Low-Dimensional Feature Spaces
.
Bayesian Deep Learning Workshop at NeurIPS
.
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Janith C. Petangoda
,
Sergio Pascoal-Diaz
,
Vincent Adam
,
Peter Vrancx
,
Jordi Grau-Moya
(2019).
Disentangled Skill Embeddings for Reinforcement Learning
.
NeurIPS Workshop on Learning Transferable Skills
.
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Steindór Sæmundsson
,
Katja Hofmann
,
Marc Deisenroth
(2019).
Variational Integrator Networks
.
Bayesian Deep Learning Workshop at NeurIPS
.
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K. S. Sesh Kumar
,
F. Bach
,
T. Pock
(2019).
Fast Decomposable Submodular Function Minimization using Constrained Total Variation
.
Advances in Neural Information Processing Systems
.
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Hugh Salimbeni
(2019).
Deep Gaussian Processes: Advances in Models and Inference
.
PDF
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K. S. Sesh Kumar
,
Marc P. Deisenroth
(2019).
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms
.
arXiv:1905.04873
.
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Hugh Salimbeni
,
Vincent Dutordoir
,
James Hensman
,
Marc P. Deisenroth
(2019).
Deep Gaussian Processes with Importance-Weighted Variational Inference
.
Proceedings of the International Conference on Machine Learning (ICML)
.
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Code
Gianfranco Bertone
,
Marc P. Deisenroth
,
Jong S. Kim
,
Sebastian Liem
,
Roberto Ruiz de Austri
,
Max Welling
(2019).
Accelerating the BSM Interpretation of LHC Data with Machine Learning
.
Physics of the Dark Universe
.
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Code
DOI
Steindór Sæmundsson
,
Alexander Terenin
,
Katja Hofmann
,
Marc P. Deisenroth
(2019).
Variational Integrator Networks for Physically Meaningful Embeddings
.
arXiv:1910.09349
.
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Simon Olofsson
,
Lukas Hebing
,
Sebastian Niedenfuehr
,
Marc Deisenroth
,
Ruth Misener
(2019).
GPdoemd: A Python Package for Design of Experiments for Model Discrimination
.
Computers and Chemical Engineering
.
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Code
Hugh Salimbeni
,
Ching-An Cheng
,
Byron Boots
,
Marc P. Deisenroth
(2018).
Orthogonally Decoupled Variational Gaussian Processes
.
Advances in Neural Information Processing Systems (NeurIPS)
.
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Code
James Wilson
,
Frank Hutter
,
Marc P. Deisenroth
(2018).
Maximizing Acquisition Functions for Bayesian Optimization
.
Advances in Neural Information Processing Systems (NeurIPS)
.
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Code
Benjamin Paul Chamberlain
(2018).
Practical Challenges of Learning andRepresentation for Large Graphs
.
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Steindór Sæmundsson
,
Katja Hofmann
,
Marc Deisenroth
(2018).
Meta Reinforcement Learning with Latent Variable Gaussian Processes
.
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)
.
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Code
Sanket Kamthe
,
Marc P. Deisenroth
(2018).
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
.
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
.
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Code
Benjamin P. Chamberlain
,
Josh Levy-Kramer
,
Clive Humby
,
Marc P. Deisenroth
(2018).
Real-Time Community Detection in Full Social Networks on a Laptop
.
PLOS ONE
.
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Code
Vincent Dutordoir
,
Hugh Salimbeni
,
Marc P. Deisenroth
,
James Hensman
(2018).
Gaussian Process Conditional Density Estimation
.
Advances in Neural Information Processing Systems (NeurIPS)
.
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Simon Olofsson
,
Marc P. Deisenroth
,
Ruth Misener
(2018).
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
.
Proceedings of the International Conference on Machine Learning
.
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Simon Olofsson
,
Mohammad Mehrian
,
Roberto Calandra
,
Liesbet Geris
,
Marc P. Deisenroth
,
Ruth Misener
(2018).
Bayesian Multi-Objective Optimisation with Mixed Analytical and Black-Box Functions: Application to Tissue Engineering
.
IEEE Transactions on Biomedical Engineering
.
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DOI
Hugh Salimbeni
,
Marc P. Deisenroth
(2017).
Doubly Stochastic Variational Inference for Deep Gaussian Processes
.
Advances in Neural Information Processing Systems (NIPS)
.
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Code
James Wilson
,
Riccardo Moriconi
,
Frank Hutter
,
Marc P. Deisenroth
(2017).
The Reparameterization Trick for Acquisition Functions
.
NIPS Workshop on Bayesian Optimization
.
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Benjamin P. Chamberlain
,
Clive Humby
,
Marc P. Deisenroth
(2017).
Probabilistic Inference of Twitter Users' Age based on What They Follow
.
Proceedings of the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)
.
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Code
Benjamin P. Chamberlain
,
James Clough
,
Marc P. Deisenroth
(2017).
Neural Embeddings of Graphs in Hyperbolic Space
.
International Workshop on Mining and Learning with Graphs
.
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Code
Andras Kupcsik
,
Marc P. Deisenroth
,
Jan Peters
,
Loh Ai Poha
,
Prahlad Vadakkepata
,
Gerhard Neumann
(2017).
Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills
.
Artificial Intelligence
.
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DOI
Stefanos Eleftheriadis
,
Thomas F. W. Nicholson
,
Marc P. Deisenroth
,
James Hensman
(2017).
Identification of Gaussian Process State Space Models
.
Advances in Neural Information Processing Systems (NIPS)
.
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Stefanos Eleftheriadis
,
Ognjen Rudovic
,
Marc P. Deisenroth
,
Maja Pantic
(2017).
Gaussian Process Domain Experts for Modeling of Facial Affect
.
IEEE Transactions on Image Processing
.
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DOI
Hugh Salimbeni
,
Marc P. Deisenroth
(2017).
Deeply Non-Stationary Gaussian Processes
.
NIPS Workshop on Bayesian Deep Learning
.
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Benjamin P. Chamberlain
,
Angelo Cardoso
,
C. H. Bryan Liu
,
Roberto Pagliari
,
Marc P. Deisenroth
(2017).
Customer Life Time Value Prediction Using Embeddings
.
Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD)
.
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Simon Olofsson
,
Mohammad Mehrian
,
Liesbet Geris
,
Roberto Calandra
,
Marc P. Deisenroth
,
Ruth Misener
(2017).
Bayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up
.
Proceedings of the European Symposium on Computer Aided Process Engineering (ESCAPE)
.
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Kai Arulkumaran
,
Marc P. Deisenroth
,
Miles Brundage
,
Anil A. Barath
(2017).
A Brief Survey of Deep Reinforcement Learning
.
IEEE Signal Processing Magazine
.
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Kshitij Tiwari
,
Valentin Honore
,
Sungmoon Jeong
,
Nak Young Chong
,
Marc P. Deisenroth
(2016).
Resource-Constrained Decentralized Active Sensing using Distributed Gaussian Processes for Multi-Robots
.
Proceedings of the International Conference on Control, Automation and Systems (ICCAS)
.
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Stefanos Eleftheriadis
,
Ognjen Rudovic
,
Marc P. Deisenroth
,
Maja Pantic
(2016).
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
.
Proceedings of the Asian Conference on Computer Vision (ACCV)
.
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Benjamin P. Chamberlain
,
Josh Levy-Kramer
,
Clive Humby
,
Marc P. Deisenroth
(2016).
Real-Time Community Detection in Large Social Networks on a Laptop
.
International Workshop on Mining and Learning with Graphs
.
Cite
Matthew C. H. Lee
,
Hugh Salimbeni
,
Marc P. Deisenroth
,
Ben Glocker
(2016).
Patch Kernels for Gaussian Processes in High-Dimensional Imaging Problems
.
NIPS Workshop on Practical Bayesian Nonparametrics
.
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Roberto Calandra
,
Jan Peters
,
Carl E. Rasmussen
,
Marc P. Deisenroth
(2016).
Manifold Gaussian Processes for Regression
.
Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN)
.
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Maciej Kurek
,
Marc P. Deisenroth
,
Wayne Luk
,
Timothy Todman
(2016).
Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs
.
Proceedings of the IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM)
.
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Hugh Salimbeni
,
Marc P. Deisenroth
(2016).
Gaussian Process Multiclass Classification with Dirichlet Priors for Imbalanced Data
.
NIPS Workshop on Practical Bayesian Nonparametrics
.
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Doniyor Ulmasov
,
Caroline Baroukh
,
Benoit Chachuat
,
Marc P. Deisenroth
,
Ruth Misener
(2016).
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems
.
Proceedings of the European Symposium on Computer Aided Process Engineering (ESCAPE)
.
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Roberto Calandra
,
André Seyfarth
,
Jan Peters
,
Marc P. Deisenroth
(2016).
Bayesian Optimization for Learning Gaits under Uncertainty
.
Annals in Mathematics and Artificial Intelligence
.
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DOI
Marc P. Deisenroth
,
Jun W. Ng
(2015).
Robust Bayesian Committee Machine for Large-Scale Gaussian Processes
.
Large-Scale Kernel Machines Workshop at ICML 2015
.
Cite
Roberto Calandra
,
Serena Ivaldi
,
Marc P. Deisenroth
,
Jan Peters
(2015).
Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin
.
Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS)
.
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Roberto Calandra
,
Serena Ivaldi
,
Marc P. Deisenroth
,
Elmar Rueckert
,
Jan Peters
(2015).
Learning Inverse Dynamics Models with Contacts
.
Proceedings of the IEEE International Conference on Robotics and Automation
.
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Niklas Wahlström
,
Thomas B. Schön
,
Marc P. Deisenroth
(2015).
Learning Deep Dynamical Models From Image Pixels
.
Proceedings of the IFAC Symposium on System Identification (SYSID)
.
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Marc P. Deisenroth
,
Dieter Fox
,
Carl E. Rasmussen
(2015).
Gaussian Processes for Data-Efficient Learning in Robotics and Control
.
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Niklas Wahlström
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Thomas B. Schön
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Marc P. Deisenroth
(2015).
From Pixels to Torques: Policy Learning with Deep Dynamical Models
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Deep Learning Workshop at ICML 2015
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Marc P. Deisenroth
,
Jun W. Ng
(2015).
Distributed Gaussian Processes
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Proceedings of the International Conference on Machine Learning (ICML)
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John-Alexander M. Assael
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Niklas Wahlström
,
Thomas B. Schön
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Marc P. Deisenroth
(2015).
Data-efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
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arXiv:1510.02173
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Bastian Bischoff
,
Duy Nguyen-Tuong
,
Herke van Hoof
,
Andrew McHutchon
,
Carl E. Rasmussen
,
Alois Knoll
,
Jan Peters
,
Marc P. Deisenroth
(2014).
Policy Search For Learning Robot Control Using Sparse Data
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
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Roberto Calandra
,
Jan Peters
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Marc P. Deisenroth
(2014).
Pareto Front Modeling for Sensitivity Analysis in Multi-Objective Bayesian Optimization
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Workshop on Bayesian Optimization in Academia and Industry at NIPS 2014
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Marc P. Deisenroth
,
Peter Englert
,
Jan Peters
,
Dieter Fox
(2014).
Multi-Task Policy Search for Robotics
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
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Sanket Kamthe
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Jan Peters
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Marc P. Deisenroth
(2014).
Multi-Modal Filtering for Non-linear Estimation
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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Arunkumar Byravan
,
Diete Fox
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Marc P. Deisenroth
(2014).
Model-based Inverse Reinforcement Learning
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Workshop on Autonomously Learning Robots at NIPS 2014
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Niklas Wahlström
,
Thomas B. Schön
,
Marc P. Deisenroth
(2014).
Learning Deep Dynamical Models From Image Pixels
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arXiv:1410.7550
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Roberto Calandra
,
Nakul Gopalan
,
André Seyfarth
,
Jan Peters
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Marc P. Deisenroth
(2014).
Bayesian Gait Optimization for Bipedal Locomotion
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Proceedings of the International Conference on Learning and Intelligent Optimization (LION)
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Nooshin HajiGhassemi
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Marc P. Deisenroth
(2014).
Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes
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Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
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Roberto Calandra
,
Jan Peters
,
André Seyfarth
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Marc P. Deisenroth
(2014).
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
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Zhikun Wang
,
Katharina Mülling
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Marc P. Deisenroth
,
Heni Ben Amor
,
David Vogt
,
Bernhard Schölkopf
,
Jan Peters
(2013).
Probabilistic Movement Modeling for Intention-based Decision Making
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International Journal of Robotics Research (IJRR)
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Peter Englert
,
Alexandros Paraschos
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Jan Peters
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Marc Deisenroth
(2013).
Probabilistic Model-based Imitation Learning
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Adaptive Behavior
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Peter Englert
,
Alexandros Paraschos
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Jan Peters
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Marc P. Deisenroth
(2013).
Model-based Imitation Learning by Probabilistic Trajectory Matching
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
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Marc P. Deisenroth
,
Peter Engert
,
Alexandros Paraschos
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Jan Peters
(2013).
Imitation Learning by Model-based Probabilistic Trajectory Matching
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Workshop on Machine Learning and Cognitive Science
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Gerhard Neumann
,
Christian Daniel
,
Andras Kupcsik
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Marc P. Deisenroth
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Jan Peters
(2013).
Hierarchical Learning of Motor Skills with Information-Theoretic Policy Search
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European Workshop on Reinforcement Learning (EWRL)
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Nakul Gopalan
,
Marc P. Deisenroth
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Jan Peters
(2013).
Feedback Error Learning for Rhythmic Motor Primitives
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
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Andras Kupcsik
,
Marc P. Deisenroth
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Jan Peters
,
Gerhard Neumann
(2013).
Data-Efficient Generalization of Robot Skills with Contextual Policy Search
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Proceedings of the AAAI Conference on Artificial Intelligence
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Roberto Calandra
,
Jan Peters
,
André Seyfarth
,
Marc P. Deisenroth
(2013).
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion
.
Workshop on Bayesian Optimization at NIPS 2013
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Peter Englert
,
Alexandros Paraschos
,
Jan Peters
,
Marc P. Deisenroth
(2013).
Addressing the Correspondence Problem by Model-based Imitation Learning
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ICRA Workshop on Autonomous Learning
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Marc P. Deisenroth
,
Gerhard Neumann
,
Jan Peters
(2013).
A Survey on Policy Search for Robotics
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Foundations and Trends in Robotics
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Marc P. Deisenroth
,
Shakir Mohamed
(2012).
Expectation Propagation in Gaussian Process Dynamical Systems
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Advances in Neural Information Processing Systems (NIPS)
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Marc P. Deisenroth
,
Roberto Calandra
,
André Seyfarth
,
Jan Peters
(2012).
Toward Fast Policy Search for Learning Legged Locomotion
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Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Marc P. Deisenroth
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Jan Peters
(2012).
Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise
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European Workshop on Reinforcement Learning (EWRL)
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Marc P. Deisenroth
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Ryan Turner
,
Marco Huber
,
Uwe D. Hanebeck
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Carl E. Rasmussen
(2012).
Robust Filtering and Smoothing with Gaussian Processes
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IEEE Transactions on Automatic Control (IEEE-TAC)
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Marc P. Deisenroth
,
Csaba Szepesvári
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Jan Peters
(2012).
Proceedings of the 10th European Workshop on Reinforcement Learning
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Zhikun Wang
,
Marc P. Deisenroth
,
Heni Ben Amor
,
David Vogt
,
Bernhard Schölkopf
,
Jan Peters
(2012).
Probabilistic Modeling of Human Dynamics for Intention Inference
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Proceedings of Robotics: Science & Systems (RSS)
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Roberto Calandra
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Tapani Raiko
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Marc P. Deisenroth
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Federico Montesino Pouzols
(2012).
Learning Deep Belief Networks from Non-Stationary Streams
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Proceedings of International Conference on Artificial Neural Networks (ICANN)
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Marc P. Deisenroth
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Peter Englert
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Alexandros Paraschos
,
Jan Peters
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Carl E. Rasmussen
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Dieter Fox
(2012).
Autonomous Planning and Control with Bayesian Nonparametric Models
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Marc P. Deisenroth
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Carl E. Rasmussen
(2011).
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
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Proceedings of the International Conference on Machine Learning (ICML)
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Marc P. Deisenroth
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Dieter Fox
(2011).
Multiple-Target Reinforcement Learning with a Single Policy
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Marc P. Deisenroth
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Carl E. Rasmussen
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Dieter Fox
(2011).
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning
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Proceedings of the International Conference on Robotics: Science and Systems (RSS)
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Marc P. Deisenroth
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Dieter Fox
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Carl E. Rasmussen
(2011).
Learning in Robotics using Bayesian Nonparametrics
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Cynthia Matuszek
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Brian Mayton
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Roberto Aimi
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Marc P. Deisenroth
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Liefeng Bo
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Robert Chu
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Michael Kung
,
Louis LeGrand
,
Joshua R. Smith
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Dieter Fox
(2011).
Gambit: An Autonomous Chess-Playing Robotic System
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Proceedings of the International Conference on Robotics and Automation (ICRA)
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Marc P. Deisenroth
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Henrik Ohlsson
(2011).
A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms
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Proceedings of the American Control Conference (ACC)
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Ryan Turner
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Marc P. Deisenroth
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Carl E. Rasmussen
(2010).
State-Space Inference and Learning with Gaussian Processes
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Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS)
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Marc P. Deisenroth
(2010).
Efficient Reinforcement Learning using Gaussian Processes
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Marc P. Deisenroth
,
Carl E. Rasmussen
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Jan Peters
(2009).
Gaussian Process Dynamic Programming
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Neurocomputing
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Marc P. Deisenroth
(2009).
Efficient Reinforcement Learning using Gaussian Processes
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Marc P. Deisenroth
,
Carl E. Rasmussen
(2009).
Efficient Reinforcement Learning for Motor Control
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Proceedings of the 10th International Workshop on Systems and Control
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Marc P. Deisenroth
,
Carl E. Rasmussen
(2009).
Bayesian Inference for Efficient Learning in Control
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Multidisciplinary Symposium on Reinforcement Learning (MSRL)
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Marc P. Deisenroth
,
Marco F. Huber
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Uwe D. Hanebeck
(2009).
Analytic Moment-based Gaussian Process Filtering
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Proceedings of the 26th International Conference on Machine Learning (ICML)
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Carl E. Rasmussen
,
Marc P. Deisenroth
(2008).
Probabilistic Inference for Fast Learning in Control
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European Workshop on Reinforcement Learning
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Marc P. Deisenroth
,
Carl E. Rasmussen
,
Jan Peters
(2008).
Model-Based Reinforcement Learning with Continuous States and Actions
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Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN)
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Marc P. Deisenroth
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Jan Peters
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Carl E. Rasmussen
(2008).
Approximate Dynamic Programming with Gaussian Processes
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Proceedings of the 2008 American Control Conference (ACC)
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Marc P. Deisenroth
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Florian Weissel
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Toshiyuki Ohtsuka
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Uwe D. Hanebeck
(2007).
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
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Proceedings of the European Control Conference 2007 (ECC)
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Marc P. Deisenroth
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Toshiyuki Ohtsuka
,
Florian Weissel
,
Dietrich Brunn
,
Uwe D. Hanebeck
(2006).
Finite-Horizon Optimal State Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle
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Proceedings of the 6th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
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Marc P. Deisenroth
(2006).
An Online Computation Approach to Optimal Finite-Horizon Control of Nonlinear Stochastic Systems
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Marc P. Deisenroth
(2004).
Toward Optimal Control of Nonlinear Systems with Continuous State Spaces
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