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D-Index & Metrics

Computer Science

D-Index
66
Citations
24694
World Ranking
2273
National Ranking
1137

Overview

J. Andrew Bagnell is affiliated with Carnegie Mellon University in the United States and specializes in the field of Computer Science. Their research primarily focuses on Artificial Intelligence, with significant contributions across several subfields including Computer Vision and Pattern Recognition, Control and Systems Engineering, Management Science and Operations Research, and Computer Networks and Communications.

The main topics of their scholarly work include:

  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Robotic Path Planning Algorithms
  • Robot Manipulation and Learning
  • Domain Adaptation and Few-Shot Learning
  • Advanced Bandit Algorithms Research
  • Evolutionary Algorithms and Applications

Bagnell has published extensively in venues such as arXiv (Cornell University), with 23 papers, and the Proceedings of the AAAI Conference on Artificial Intelligence, contributing 2 publications. Selected recent papers include:

  • Feedback in Imitation Learning: The Three Regimes of Covariate Shift (2021), arXiv (Cornell University)
  • Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient (2022), arXiv (Cornell University)
  • CMAX++: Leveraging Experience in Planning and Execution using Inaccurate Models (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • Causal Imitation Learning under Temporally Correlated Noise (2022), arXiv (Cornell University)
  • Sequence Model Imitation Learning with Unobserved Contexts (2022), arXiv (Cornell University)

Frequent collaborators in Bagnell's research include Sanjiban Choudhury, Gokul Swamy, Zhiwei Steven Wu, Anirudh Vemula, and Yuda Song. These partnerships have resulted in multiple joint works, reflecting engagement with a range of topics primarily within reinforcement learning and robotics.

Best Publications

  • Reinforcement learning in robotics: A survey

    Jens Kober;J. Andrew Bagnell;Jan Peters

  • Maximum entropy inverse reinforcement learning

    Brian D. Ziebart;Andrew Maas;J. Andrew Bagnell;Anind K. Dey

  • A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

    Stéphane Ross;Geoffrey J. Gordon;J. Andrew Bagnell

  • CHOMP: Gradient optimization techniques for efficient motion planning

    Nathan Ratliff;Matt Zucker;J. Andrew Bagnell;Siddhartha Srinivasa

  • Encyclopedia of Machine Learning and Data Mining

    Unknown

  • Maximum margin planning

    Nathan D. Ratliff;J. Andrew Bagnell;Martin A. Zinkevich

  • CHOMP: Covariant Hamiltonian optimization for motion planning

    Matt Zucker;Nathan Ratliff;Anca D. Dragan;Mihail Pivtoraiko

  • An Algorithmic Perspective on Imitation Learning

    Takayuki Osa;Joni Pajarinen;Gerhard Neumann;J. Andrew Bagnell

  • Planning-based prediction for pedestrians

    Brian D. Ziebart;Nathan Ratliff;Garratt Gallagher;Christoph Mertz

  • Learning monocular reactive UAV control in cluttered natural environments

    Stephane Ross;Narek Melik-Barkhudarov;Kumar Shaurya Shankar;Andreas Wendel

  • Modeling purposeful adaptive behavior with the principle of maximum causal entropy

    J. Andrew Bagnell;Brian D. Ziebart

  • Autonomous helicopter control using reinforcement learning policy search methods

    J.A. Bagnell;J.G. Schneider

  • Contextual classification with functional Max-Margin Markov Networks

    Daniel Munoz;J Andrew Bagnell;Nicolas Vandapel;Martial Hebert

  • Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior

    Brian D. Ziebart;Andrew L. Maas;Anind K. Dey;J. Andrew Bagnell

  • Pose Machines: Articulated Pose Estimation via Inference Machines

    Varun Ramakrishna;Daniel Munoz;Martial Hebert;James Andrew Bagnell

  • Learning to search: Functional gradient techniques for imitation learning

    Nathan D. Ratliff;David Silver;J. Andrew Bagnell

  • Stacked hierarchical labeling

    Daniel Munoz;J. Andrew Bagnell;Martial Hebert

  • Modeling Interaction via the Principle of Maximum Causal Entropy

    Brian D. Ziebart;J. A. Bagnell;Anind K. Dey

  • Improving multi-step prediction of learned time series models

    Arun Venkatraman;Martial Hebert;J. Andrew Bagnell

  • Online) Subgradient Methods for Structured Prediction

    Nathan D. Ratliff;J. Andrew Bagnell;Martin A. Zinkevich

  • Reinforcement and Imitation Learning via Interactive No-Regret Learning

    Stéphane Ross;J. Andrew Bagnell

Frequent Co-Authors

Martial Hebert
Martial Hebert Carnegie Mellon University
Anthony Stentz
Anthony Stentz Carnegie Mellon University
Siddhartha S. Srinivasa
Siddhartha S. Srinivasa University of Washington
Anind K. Dey
Anind K. Dey University of Washington
Byron Boots
Byron Boots University of Washington
Geoffrey J. Gordon
Geoffrey J. Gordon Carnegie Mellon University
Nancy S. Pollard
Nancy S. Pollard Carnegie Mellon University
David Silver
David Silver DeepMind (United Kingdom)
Andrew B. Schwartz
Andrew B. Schwartz University of Pittsburgh
Matthew T. Mason
Matthew T. Mason Carnegie Mellon University

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