Bagnell, J. Andrew

35 publications

NeurIPS 2024 REBEL: Reinforcement Learning via Regressing Relative Rewards Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun

ICMLW 2024 REBEL: Reinforcement Learning via Regressing Relative Rewards Zhaolin Gao, Jonathan Daniel Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun

ICMLW 2024 REBEL: Reinforcement Learning via Regressing Relative Rewards Zhaolin Gao, Jonathan Daniel Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun

NeurIPS 2024 The Importance of Online Data: Understanding Preference Fine-Tuning via Coverage Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun

L4DC 2022 On the Effectiveness of Iterative Learning Control Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell

AAAI 2021 CMAX++ : Leveraging Experience in Planning and Execution Using Inaccurate Models Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev

ICML 2021 Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu

AAAI 2019 Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell

ICLR 2018 Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning Wen Sun, J. Andrew Bagnell, Byron Boots

ICML 2017 Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell

AISTATS 2017 Gradient Boosting on Stochastic Data Streams Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

ECCV 2016 A Discriminative Framework for Anomaly Detection in Large Videos Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

UAI 2016 Efficient Feature Group Sequencing for Anytime Linear Prediction Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert

IJCAI 2016 Inference Machines for Nonparametric Filter Learning Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell

ICML 2016 Learning to Filter with Predictive State Inference Machines Wen Sun, Arun Venkatraman, Byron Boots, J.Andrew Bagnell

UAI 2016 Learning to Smooth with Bidirectional Predictive State Inference Machines Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots

IJCAI 2016 Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees Wen Sun, J. Andrew Bagnell

AAAI 2016 Online Instrumental Variable Regression with Applications to Online Linear System Identification Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots

AAAI 2015 Improving Multi-Step Prediction of Learned Time Series Models Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

UAI 2015 Online Bellman Residual Algorithms with Predictive Error Guarantees Wen Sun, J. Andrew Bagnell

ICCV 2015 Predicting Multiple Structured Visual Interpretations Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell

AAAI 2015 Submodular Surrogates for Value of Information Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew Bagnell, Siddhartha S. Srinivasa, Andreas Krause

CVPR 2011 Learning Message-Passing Inference Machines for Structured Prediction Stéphane Ross, Daniel Munoz, Martial Hebert, J. Andrew Bagnell

ICML 2010 Boosted Backpropagation Learning for Training Deep Modular Networks Alexander Grubb, J. Andrew Bagnell

ICML 2010 Modeling Interaction via the Principle of Maximum Causal Entropy Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey

ECCV 2010 Stacked Hierarchical Labeling Daniel Munoz, J. Andrew Bagnell, Martial Hebert

UAI 2009 Convex Coding David M. Bradley, J. Andrew Bagnell

AISTATS 2009 Inverse Optimal Heuristic Control for Imitation Learning Nathan Ratliff, Brian Ziebart, Kevin Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha Srinivasa

AAAI 2008 Maximum Entropy Inverse Reinforcement Learning Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey

AISTATS 2007 (Approximate) Subgradient Methods for Structured Prediction Nathan D. Ratliff, J. Andrew Bagnell, Martin A. Zinkevich

IJCAI 2007 Kernel Conjugate Gradient for Fast Kernel Machines Nathan D. Ratliff, J. Andrew Bagnell

ICML 2006 Maximum Margin Planning Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich

AAAI 2005 Robust Supervised Learning J. Andrew Bagnell

IJCAI 2003 Covariant Policy Search J. Andrew Bagnell, Jeff G. Schneider

UAI 2002 Learning with Scope, with Application to Information Extraction and Classification David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum