World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
11296
World Ranking
3297
National Ranking
1595

Overview

Jeff Schneider is affiliated with Carnegie Mellon University in the United States. Their research is concentrated in the field of Computer Science, with a strong focus on several specialized subfields including Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Control and Systems Engineering, and Computer Vision and Pattern Recognition.

Their work engages with a variety of main topics such as Reinforcement Learning in Robotics, Machine Learning and Algorithms, Optimization and Search Problems, Adversarial Robustness in Machine Learning, Auction Theory and Applications, Data Stream Mining Techniques, and Autonomous Vehicle Technology and Safety.

Recent publications by Jeff Schneider include:

  • "When is Deep Learning the Best Approach to Knowledge Tracing?" (2020) published in Zenodo (CERN European Organization for Nuclear Research)
  • "Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification" (2021) published in arXiv (Cornell University)
  • "Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification" (2020) published in arXiv (Cornell University)
  • "Multi-Agent Active Search: A Reinforcement Learning Approach" (2021) published in IEEE Robotics and Automation Letters

Jeff Schneider frequently publishes in venues such as arXiv (Cornell University), where they have 47 publications, IEEE Robotics and Automation Letters with 2 publications, Nuclear Fusion with 2 publications, Zenodo (CERN European Organization for Nuclear Research), and the 2021 60th IEEE Conference on Decision and Control (CDC).

They have collaborated regularly with several coauthors, including Willie Neiswanger, Ian Char, Youngseog Chung, Viraj Mehta, and Ramina Ghods.

Best Publications

  • Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

    Liang Xiong;Xi Chen;Tzu-Kuo Huang;Jeff G. Schneider

  • Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

    Henggang Cui;Vladan Radosavljevic;Fang-Chieh Chou;Tsung-Han Lin

  • Neural Architecture Search with Bayesian Optimisation and Optimal Transport

    Kirthevasan Kandasamy;Willie Neiswanger;Jeff Schneider;Barnabas Poczos

  • Autonomous helicopter control using reinforcement learning policy search methods

    J.A. Bagnell;J.G. Schneider

  • Efficiently learning the accuracy of labeling sources for selective sampling

    Pinar Donmez;Jaime G. Carbonell;Jeff Schneider

  • Detecting anomalous records in categorical datasets

    Kaustav Das;Jeff Schneider

  • Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs

    Rosemary Emery-Montemerlo;Geoff Gordon;Jeff Schneider;Sebastian Thrun

  • High Dimensional Bayesian Optimisation and Bandits via Additive Models

    Kirthevasan Kandasamy;Jeff Schneider;Barnabas Poczos

  • Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

    Nemanja Djuric;Vladan Radosavljevic;Henggang Cui;Thi Nguyen

  • Controlling the False-Discovery Rate in Astrophysical Data Analysis

    Christopher J. Miller;Christopher Genovese;Robert C. Nichol;Larry Wasserman

  • Policy Search by Dynamic Programming

    J. A. Bagnell;Sham M Kakade;Jeff G. Schneider;Andrew Y. Ng

  • Distributed Value Functions

    Jeff G. Schneider;Weng-Keen Wong;Andrew W. Moore;Martin A. Riedmiller

  • Covariant policy search

    J. Andrew Bagnell;Jeff Schneider

  • Multi-Label Output Codes using Canonical Correlation Analysis

    Yi Zhang;Jeff G. Schneider

  • Deep Learning with Sets and Point Clouds

    Siamak Ravanbakhsh;Jeff G. Schneider;Barnabás Póczos

  • Parallelised Bayesian Optimisation via Thompson Sampling

    Kirthevasan Kandasamy;Akshay Krishnamurthy;Jeff Schneider;Barnabás Póczos

  • Anomaly pattern detection in categorical datasets

    Kaustav Das;Jeff Schneider;Daniel B. Neill

  • Multi-fidelity Bayesian optimisation with continuous approximations

    Kirthevasan Kandasamy;Gautam Dasarathy;Jeff Schneider;Barnabás Póczos

  • Efficient Locally Weighted Polynomial Regression Predictions

    Andrew W. Moore;Jeff Schneider;Kan Deng

  • Automatic construction of active appearance models as an image coding problem

    S. Baker;I. Matthews;J. Schneider

  • On the error of random fourier features

    Dougal J. Sutherland;Jeff Schneider

Frequent Co-Authors

Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Andrew W. Moore
Andrew W. Moore Carnegie Mellon University
Robert C. Nichol
Robert C. Nichol University of Surrey
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
John M. Dolan
John M. Dolan Carnegie Mellon University
Alexander S. Szalay
Alexander S. Szalay Johns Hopkins University
Howie Choset
Howie Choset Carnegie Mellon University
Idit Zehavi
Idit Zehavi Case Western Reserve University
Andrew M. Hopkins
Andrew M. Hopkins Macquarie University

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