H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 45 Citations 7,208 156 World Ranking 3503 National Ranking 1805

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

Jeff Schneider mainly investigates Artificial intelligence, Machine learning, Mathematical optimization, Bayesian probability and Algorithm. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Data mining, Categorical variable and Pattern recognition. His study in the field of Thompson sampling also crosses realms of Task analysis.

Jeff Schneider has included themes like Function, Probability distribution, Covariant transformation and Reinforcement learning in his Mathematical optimization study. His Bayesian probability research is multidisciplinary, incorporating elements of Additive model, Structure, Key and Regret. His Algorithm study combines topics in areas such as Point cloud, Equivariant map and Permutation.

His most cited work include:

  • Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization (440 citations)
  • Autonomous helicopter control using reinforcement learning policy search methods (244 citations)
  • Efficiently learning the accuracy of labeling sources for selective sampling (183 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Machine learning, Algorithm, Mathematical optimization and Data mining. As a part of the same scientific family, Jeff Schneider mostly works in the field of Artificial intelligence, focusing on State and, on occasion, Motion prediction. The various areas that Jeff Schneider examines in his Machine learning study include Motion and Trajectory.

His Algorithm research integrates issues from Estimator, Kernel and Cluster analysis. His Mathematical optimization research includes themes of Function, Regret, Gaussian process and Reinforcement learning. His Anomaly detection study, which is part of a larger body of work in Data mining, is frequently linked to Detector, bridging the gap between disciplines.

He most often published in these fields:

  • Artificial intelligence (46.27%)
  • Machine learning (29.41%)
  • Algorithm (16.86%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (46.27%)
  • Machine learning (29.41%)
  • Trajectory (4.31%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Machine learning, Trajectory, Reinforcement learning and Bayesian optimization. Particularly relevant to Deep learning is his body of work in Artificial intelligence. In his study, Hyperparameter and Probabilistic logic is inextricably linked to Bayesian inference, which falls within the broad field of Machine learning.

In his research on the topic of Reinforcement learning, Supervised learning, Visualization and Optimal control is strongly related with Human–computer interaction. His biological study spans a wide range of topics, including Search algorithm, Gaussian process and Compressed sensing. Jeff Schneider interconnects Graph, Kernel, Optimization problem, Mathematical optimization and Sample in the investigation of issues within Gaussian process.

Between 2018 and 2021, his most popular works were:

  • Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks (154 citations)
  • Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving (28 citations)
  • Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets (28 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Trajectory, Motion and Gaussian process. His Artificial intelligence study typically links adjacent topics like State. His study in the fields of Hyperparameter under the domain of Machine learning overlaps with other disciplines such as Component.

His Hyperparameter research is multidisciplinary, incorporating perspectives in Artificial neural network and Bayesian probability. He has researched Trajectory in several fields, including Robotics, Hidden Markov model and Object based. His work deals with themes such as Bayesian optimization, Mathematical optimization, Black box function, Value and Robot, which intersect with Gaussian process.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

Liang Xiong;Xi Chen;Tzu-Kuo Huang;Jeff G. Schneider.
siam international conference on data mining (2010)

667 Citations

Autonomous helicopter control using reinforcement learning policy search methods

J.A. Bagnell;J.G. Schneider.
international conference on robotics and automation (2001)

377 Citations

Efficiently learning the accuracy of labeling sources for selective sampling

Pinar Donmez;Jaime G. Carbonell;Jeff Schneider.
knowledge discovery and data mining (2009)

271 Citations

Detecting anomalous records in categorical datasets

Kaustav Das;Jeff Schneider.
knowledge discovery and data mining (2007)

240 Citations

Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs

Rosemary Emery-Montemerlo;Geoff Gordon;Jeff Schneider;Sebastian Thrun.
adaptive agents and multi-agents systems (2004)

232 Citations

Policy Search by Dynamic Programming

J. A. Bagnell;Sham M Kakade;Jeff G. Schneider;Andrew Y. Ng.
neural information processing systems (2003)

206 Citations

Controlling the False-Discovery Rate in Astrophysical Data Analysis

Christopher J. Miller;Christopher Genovese;Robert C. Nichol;Larry Wasserman.
The Astronomical Journal (2001)

200 Citations

Distributed Value Functions

Jeff G. Schneider;Weng-Keen Wong;Andrew W. Moore;Martin A. Riedmiller.
international conference on machine learning (1999)

193 Citations

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Kirthevasan Kandasamy;Willie Neiswanger;Jeff Schneider;Barnabas Poczos.
neural information processing systems (2018)

169 Citations

Covariant policy search

J. Andrew Bagnell;Jeff Schneider.
international joint conference on artificial intelligence (2003)

159 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Top Scientists Citing Jeff Schneider

Jan Peters

Jan Peters

TU Darmstadt

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Sergey Levine

University of California, Berkeley

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Marc Peter Deisenroth

University College London

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Barnabás Póczos

Carnegie Mellon University

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Shlomo Zilberstein

University of Massachusetts Amherst

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Richard A. Houghten

Richard A. Houghten

Torrey Pines Institute For Molecular Studies

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Raquel Urtasun

Raquel Urtasun

University of Toronto

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Pieter Abbeel

Pieter Abbeel

University of California, Berkeley

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Leslie Pack Kaelbling

Leslie Pack Kaelbling

MIT

Publications: 23

Dacheng Tao

Dacheng Tao

University of Sydney

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Mingsheng Long

Mingsheng Long

Tsinghua University

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Jianmin Wang

Jianmin Wang

Tsinghua University

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Alfred O. Hero

Alfred O. Hero

University of Michigan–Ann Arbor

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Andrew W. Moore

Andrew W. Moore

University of Cambridge

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Jaime G. Carbonell

Jaime G. Carbonell

Carnegie Mellon University

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