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Computer Science

D-Index
46
Citations
14660
World Ranking
6681
National Ranking
400

Overview

Marc Peter Deisenroth is affiliated with University College London in the United Kingdom. The primary field of study is Computer Science, with a substantial focus on Artificial Intelligence. The body of work also spans related subfields such as Control and Systems Engineering, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, and Statistical and Nonlinear Physics.

The main topics of research include:

  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Algorithms
  • Neural Networks and Applications
  • Machine Learning and Data Classification
  • Advanced Multi-Objective Optimization Algorithms
  • Model Reduction and Neural Networks
  • Robot Manipulation and Learning

The scientist has published extensively in various venues, with a significant number of papers appearing on arXiv (Cornell University). Other publication venues include IEEE Robotics and Automation Letters, Environmental Data Science, Nuclear Fusion, and Machine Learning journals.

Selected recent papers are:

  • "Copula Flows for Synthetic Data Generation," 2021, arXiv (Cornell University)
  • "Matérn Gaussian processes on Riemannian manifolds," 2020, arXiv (Cornell University)
  • "High-dimensional Bayesian optimization using low-dimensional feature spaces," 2020, Machine Learning
  • "Plasma surrogate modelling using Fourier neural operators," 2024, Nuclear Fusion
  • "Efficiently Sampling Functions from Gaussian Process Posteriors," 2020, arXiv (Cornell University)

Through their work, the scientist has collaborated frequently with several other researchers, including A. Aldo Faisal, Cheng Soon Ong, So Takao, Yasemin Bekiroglu, and Alexander Terenin.

Marc Peter Deisenroth has also contributed to academic literature by authoring a book titled Mathematics for Machine Learning, published by Cambridge University Press in 2020.

Best Publications

  • Deep Reinforcement Learning: A Brief Survey

    Kai Arulkumaran;Marc Peter Deisenroth;Miles Brundage;Anil Anthony Bharath

  • PILCO: A Model-Based and Data-Efficient Approach to Policy Search

    Marc Deisenroth;Carl E. Rasmussen

  • A Survey on Policy Search for Robotics

    Marc Peter Deisenroth;Gerhard Neumann;Jan Peters

  • Gaussian Processes for Data-Efficient Learning in Robotics and Control

    Marc Peter Deisenroth;Dieter Fox;Carl Edward Rasmussen

  • A brief survey of deep reinforcement learning

    Kai Arulkumaran;Marc Peter Deisenroth;Miles Brundage;Anil Anthony Bharath

  • Mathematics for Machine Learning

    Marc Peter Deisenroth;A. Aldo Faisal;Cheng Soon Ong

  • Bayesian optimization for learning gaits under uncertainty

    Roberto Calandra;André Seyfarth;Jan Peters;Marc Peter Deisenroth

  • Doubly Stochastic Variational Inference for Deep Gaussian Processes

    Hugh Salimbeni;Marc Peter Deisenroth

  • Gaussian process dynamic programming

    Marc Peter Deisenroth;Carl Edward Rasmussen;Jan Peters

  • Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning

    Marc Peter Deisenroth;Carl Edward Rasmussen;Dieter Fox

  • Distributed Gaussian Processes

    Marc Deisenroth;Jun Wei Ng

  • Manifold Gaussian Processes for regression

    Roberto Calandra;Jan Peters;Carl Edward Rasmussen;Marc Peter Deisenroth

  • Efficient Reinforcement Learning Using Gaussian Processes

    Marc Peter Deisenroth

  • Probabilistic movement modeling for intention inference in human-robot interaction

    Zhikun Wang;Katharina Mülling;Marc Peter Deisenroth;Heni Ben Amor

  • Analytic moment-based Gaussian process filtering

    Marc Peter Deisenroth;Marco F. Huber;Uwe D. Hanebeck

  • Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

    Sanket Kamthe;Marc Peter Deisenroth

  • Robust Filtering and Smoothing with Gaussian Processes

    M. P. Deisenroth;R. D. Turner;M. F. Huber;U. D. Hanebeck

  • Maximizing acquisition functions for Bayesian optimization

    James T. Wilson;Frank Hutter;Marc Peter Deisenroth

  • From Pixels to Torques: Policy Learning with Deep Dynamical Models

    Niklas Wahlström;Thomas B. Schön;Marc Peter Deisenroth

  • Multi-Task Policy Search for Robotics

    Marc Peter Deisenroth;Peter Englert;Jan Peters;Dieter Fox

  • Approximate dynamic programming with Gaussian processes

    M.P. Deisenroth;J. Peters;C.E. Rasmussen

  • Efficiently sampling functions from Gaussian process posteriors

    James Wilson;Viacheslav Borovitskiy;Alexander Terenin;Peter Mostowsky

Frequent Co-Authors

Jan Peters
Jan Peters Technical University of Darmstadt
Carl Edward Rasmussen
Carl Edward Rasmussen University of Cambridge
Maja Pantic
Maja Pantic Imperial College London
Dieter Fox
Dieter Fox University of Washington
Uwe D. Hanebeck
Uwe D. Hanebeck Karlsruhe Institute of Technology
Andre Seyfarth
Andre Seyfarth Technical University of Darmstadt
Thomas B. Schön
Thomas B. Schön Uppsala University
Gerhard Neumann
Gerhard Neumann Karlsruhe Institute of Technology
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Frank Hutter
Frank Hutter University of Freiburg

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