World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
79
Citations
60831
World Ranking
1109
National Ranking
59

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

Nando de Freitas is affiliated with DeepMind in the United Kingdom. Their research primarily falls within computer science, with a focus on artificial intelligence reflected in a significant proportion of their work. Their subfields of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Computational Theory and Mathematics

The scientist's research topics encompass a variety of areas related to machine learning and its applications. These include:

  • Reinforcement Learning in Robotics
  • Topic Modeling
  • Machine Learning and Data Classification
  • Advanced Bandit Algorithms Research
  • Adversarial Robustness in Machine Learning
  • Natural Language Processing Techniques
  • Data Stream Mining Techniques

De Freitas has published multiple papers across several respected venues, with a notable presence on arXiv (Cornell University), where they contributed 23 papers. Other publication venues include the International Journal of Innovative Science and Research Technology (IJISRT), Nature, Computational Linguistics, and Science.

Some recent publications include:

  • Restoring and attributing ancient texts using deep neural networks, 2022, Nature
  • Critic Regularized Regression, 2020, arXiv (Cornell University)
  • Acme: A Research Framework for Distributed Reinforcement Learning, 2020, arXiv (Cornell University)
  • A Generalist Agent, 2022, arXiv (Cornell University)
  • Machine Learning for Ancient Languages: A Survey, 2023, Computational Linguistics

Frequent co-authors with whom de Freitas has collaborated include:

  • Konrad Żołna
  • Çaǧlar Gülçehre
  • Scott Reed
  • Yutian Chen
  • Ksenia Konyushkova

Best Publications

  • Sequential Monte Carlo methods in practice

    Arnaud Doucet;Nando De Freitas;Neil Gordon;Adrian Smith

  • An Introduction to Sequential Monte Carlo Methods

    Arnaud Doucet;Nando de Freitas;Neil J. Gordon

  • Taking the Human Out of the Loop: A Review of Bayesian Optimization

    Bobak Shahriari;Kevin Swersky;Ziyu Wang;Ryan P. Adams

  • An introduction to MCMC for machine learning

    Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan

  • Dueling network architectures for deep reinforcement learning

    Ziyu Wang;Tom Schaul;Matteo Hessel;Hado Van Hasselt

  • A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

    Eric Brochu;Vlad M. Cora;Nando de Freitas

  • The Unscented Particle Filter

    Rudolph van der Merwe;Arnaud Doucet;Nando de Freitas;Eric A. Wan

  • Matching words and pictures

    Kobus Barnard;Pinar Duygulu;David Forsyth;Nando de Freitas

  • Learning to learn by gradient descent by gradient descent

    Marcin Andrychowicz;Misha Denil;Sergio Gomez;Matthew W. Hoffman

  • A Boosted Particle Filter: Multitarget Detection and Tracking

    Kenji Okuma;Ali Taleghani;Nando de Freitas;James J. Little

  • Rao-blackwellised particle filtering for dynamic Bayesian networks

    Arnaud Doucet;Nando de Freitas;Kevin P. Murphy;Stuart J. Russell

  • Learning to Communicate with Deep Multi-Agent Reinforcement Learning

    Jakob N. Foerster;Yannis M. Assael;Nando de Freitas;Shimon Whiteson

  • Predicting Parameters in Deep Learning

    Misha Denil;Babak Shakibi;Laurent Dinh;Marc'Aurelio Ranzato

  • Learning to Communicate with Deep Multi−Agent Reinforcement Learning

    Jakob Foerster;Ioannis Alexandros Assael;Nando de Freitas;Shimon Whiteson

  • A Generalist Agent

    Unknown

  • Bayesian optimization in a billion dimensions via random embeddings

    Ziyu Wang;Frank Hutter;Masrour Zoghi;David Matheson

  • Sample Efficient Actor-Critic with Experience Replay.

    Ziyu Wang;Victor Bapst;Nicolas Heess;Volodymyr Mnih

  • A Statistical Model for General Contextual Object Recognition

    Peter Carbonetto;Nando de Freitas;Kobus Barnard

  • Neural Programmer-Interpreters

    Scott Reed;Nando de Freitas

  • Sequential Monte Carlo in Practice

    Arnaud Doucet;Nando de Freitas;Neil J. Gordon

  • LipNet: End-to-End Sentence-level Lipreading

    Yannis M. Assael;Brendan Shillingford;Shimon Whiteson;Nando de Freitas

Frequent Co-Authors

Arnaud Doucet
Arnaud Doucet University of Oxford
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Aaron van den Oord
Aaron van den Oord Google (United States)
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
David Poole
David Poole University of British Columbia
Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Matthew Botvinick
Matthew Botvinick Yale University
Kevin Swersky
Kevin Swersky Google (United States)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)

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