J
Jeff Schneider
Researcher at Carnegie Mellon University
Publications - 279
Citations - 11087
Jeff Schneider is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 55, co-authored 265 publications receiving 9624 citations. Previous affiliations of Jeff Schneider include General Motors & University of Rochester.
Papers
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Proceedings ArticleDOI
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
TL;DR: This work proposes a factor-based algorithm that is able to take time into account, and provides a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control.
Proceedings ArticleDOI
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Henggang Cui,Vladan Radosavljevic,Fang-Chieh Chou,Tsung-Han Lin,Thi Nguyen,Tzu-Kuo Huang,Jeff Schneider,Nemanja Djuric +7 more
TL;DR: This work presents a method to predict multiple possible trajectories of actors while also estimating their probabilities, and successfully tested on SDVs in closed-course tests.
Proceedings ArticleDOI
Autonomous helicopter control using reinforcement learning policy search methods
J.A. Bagnell,Jeff Schneider +1 more
TL;DR: This work considers algorithms that evaluate and synthesize controllers under distributions of Markovian models and demonstrates the presented learning control algorithm by flying an autonomous helicopter and shows that the controller learned is robust and delivers good performance in this real-world domain.
Proceedings ArticleDOI
Efficiently learning the accuracy of labeling sources for selective sampling
TL;DR: IEThresh (Interval Estimate Threshold) is presented as a strategy to intelligently select the expert(s) with the highest estimated labeling accuracy and achieves a given level of accuracy with less than half the queries issued by all-experts labeling and less than a third the queries required by random expert selection on datasets such as the UCI mushroom one.
Proceedings Article
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
TL;DR: NASHBOT is developed, a Gaussian process based BO framework for neural architecture search which outperforms other alternatives for architecture search in several cross validation based model selection tasks on multi-layer perceptrons and convolutional neural networks.