J. Andrew Bagnell

166 papers A* 41A 14C 1Misc 1Journal 82Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Expanding the Capabilities of Reinforcement Learning via Text Feedback. CoRR Yuda Song, Lili Chen, Fahim Tajwar, Rémi Munos, Deepak Pathak, J. Andrew Bagnell, Aarti Singh, Andrea Zanette
2025	J	`jnl`	All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning. CoRR Gokul Swamy, Sanjiban Choudhury, Wen Sun, Zhiwei Steven Wu, J. Andrew Bagnell
2025	J	`jnl`	To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning. CoRR Yuda Song, Dhruv Rohatgi, Aarti Singh, J. Andrew Bagnell
2024	J	`jnl`	Hybrid Inverse Reinforcement Learning. CoRR Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury
2024	J	`jnl`	Hybrid Reinforcement Learning from Offline Observation Alone. CoRR Yuda Song, J. Andrew Bagnell, Aarti Singh
2024	J	`jnl`	REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun
2024	A*	`conf`	The Importance of Online Data: Understanding Preference Fine-tuning via Coverage. NeurIPS Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun
2024	J	`jnl`	Understanding Preference Fine-Tuning Through the Lens of Coverage. CoRR Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun
2023	J	`jnl`	Inverse Reinforcement Learning without Reinforcement Learning. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2023	J	`jnl`	The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. CoRR Anirudh Vemula, Yuda Song, Aarti Singh, J. Andrew Bagnell, Sanjiban Choudhury
2022	J	`jnl`	Causal Imitation Learning under Temporally Correlated Noise. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	J	`jnl`	Game-Theoretic Algorithms for Conditional Moment Matching. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	J	`jnl`	Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
2022	A*	`conf`	Minimax Optimal Online Imitation Learning via Replay Estimation. NeurIPS Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran
2022	J	`jnl`	Minimax Optimal Online Imitation Learning via Replay Estimation. CoRR Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran
2022	—	`conf`	On the Effectiveness of Iterative Learning Control. L4DC Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
2022	A*	`conf`	Sequence Model Imitation Learning with Unobserved Contexts. NeurIPS Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	J	`jnl`	Sequence Model Imitation Learning with Unobserved Contexts. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2021	J	`jnl`	A Critique of Strictly Batch Imitation Learning. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2021	A*	`conf`	CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models. AAAI Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev
2021	J	`jnl`	Feedback in Imitation Learning: The Three Regimes of Covariate Shift. CoRR Jonathan C. Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian D. Ziebart, J. Andrew Bagnell
2021	A*	`conf`	Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap. ICML Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu
2021	J	`jnl`	Of Moments and Matching: Trade-offs and Treatments in Imitation Learning. CoRR Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell
2021	J	`jnl`	On the Effectiveness of Iterative Learning Control. CoRR Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
2020	J	`jnl`	CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models. CoRR Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev
2020	J	`jnl`	Exploration in Action Space. CoRR Anirudh Vemula, Wen Sun, J. Andrew Bagnell
2020	—	`conf`	Planning and Execution using Inaccurate Models with Provable Guarantees. Robotics: Science and Systems Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev
2020	J	`jnl`	Planning and Execution using Inaccurate Models with Provable Guarantees. CoRR Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev
2020	J	`jnl`	TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions. CoRR Anirudh Vemula, J. Andrew Bagnell
2020	—	`conf`	Tron: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions. CDC Anirudh Vemula, J. Andrew Bagnell
2019	A	`conf`	Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. AISTATS Anirudh Vemula, Wen Sun, J. Andrew Bagnell
2019	J	`jnl`	Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. CoRR Anirudh Vemula, Wen Sun, J. Andrew Bagnell
2019	A*	`conf`	Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. AAAI Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell
2019	J	`jnl`	Provably Efficient Imitation Learning from Observation Alone. CoRR Wen Sun, Anirudh Vemula, Byron Boots, J. Andrew Bagnell
2018	J	`jnl`	An Algorithmic Perspective on Imitation Learning. CoRR Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters
2018	J	`jnl`	An Algorithmic Perspective on Imitation Learning. Found. Trends Robotics Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters
2018	A*	`conf`	Dual Policy Iteration. NeurIPS Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
2018	J	`jnl`	Dual Policy Iteration. CoRR Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
2018	J	`jnl`	Shared autonomy via hindsight optimization for teleoperation and teaming. Int. J. Robotics Res. Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, J. Andrew Bagnell
2018	J	`jnl`	Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. CoRR Wen Sun, J. Andrew Bagnell, Byron Boots
2018	—	`conf`	Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. ICLR (Poster) Wen Sun, J. Andrew Bagnell, Byron Boots
2017	J	`jnl`	A Fast Stochastic Contact Model for Planar Pushing and Grasping: Theory and Experimental Validation. CoRR Jiaji Zhou, J. Andrew Bagnell, Matthew T. Mason
2017	J	`jnl`	A Probabilistic Planning Framework for Planar Grasping Under Uncertainty. IEEE Robotics Autom. Lett. Jiaji Zhou, Robert Paolini, Aaron M. Johnson, J. Andrew Bagnell, Matthew T. Mason
2017	J	`jnl`	Anytime Neural Networks via Joint Optimization of Auxiliary Losses. CoRR Hanzhang Hu, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert
2017	J	`jnl`	Autonomy infused teleoperation with application to brain computer interface controlled manipulation. Auton. Robots Katharina Mülling, Arun Venkatraman, Jean-Sebastien Valois, John Downey, Jeffrey M. Weiss, Shervin Javdani, Martial Hebert, Andrew B. Schwartz, Jennifer L. Collinger, J. Andrew Bagnell
2017	A*	`conf`	Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. ICML Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
2017	J	`jnl`	Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. CoRR Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
2017	A	`conf`	Gradient Boosting on Stochastic Data Streams. AISTATS Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
2017	J	`jnl`	Gradient Boosting on Stochastic Data Streams. CoRR Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
2017	J	`jnl`	Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection. CoRR Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
2017	J	`jnl`	Log-DenseNet: How to Sparsify a DenseNet. CoRR Hanzhang Hu, Debadeepta Dey, Allison Del Giorno, Martial Hebert, J. Andrew Bagnell
2017	—	`ch.`	Policy Gradient Methods. Encyclopedia of Machine Learning and Data Mining Jan Peters, J. Andrew Bagnell
2017	J	`jnl`	Predictive-State Decoders: Encoding the Future into Recurrent Networks. CoRR Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell
2017	J	`jnl`	Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming. CoRR Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, J. Andrew Bagnell
2016	J	`jnl`	A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application. CoRR Jiaji Zhou, Robert Paolini, J. Andrew Bagnell, Matthew T. Mason
2016	—	`conf`	A Discriminative Framework for Anomaly Detection in Large Videos. ECCV (5) Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
2016	J	`jnl`	A Discriminative Framework for Anomaly Detection in Large Videos. CoRR Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
2016	A*	`conf`	A convex polynomial force-motion model for planar sliding: Identification and application. ICRA Jiaji Zhou, Robert Paolini, J. Andrew Bagnell, Matthew T. Mason
2016	A	`conf`	Efficient Feature Group Sequencing for Anytime Linear Prediction. UAI Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert
2016	—	`conf`	Improved Learning of Dynamics Models for Control. ISER Arun Venkatraman, Roberto Capobianco, Lerrel Pinto, Martial Hebert, Daniele Nardi, J. Andrew Bagnell
2016	A*	`conf`	Inference Machines for Nonparametric Filter Learning. IJCAI Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell
2016	J	`jnl`	Introspective Perception: Learning to Predict Failures in Vision Systems. CoRR Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert
2016	A	`conf`	Introspective perception: Learning to predict failures in vision systems. IROS Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert
2016	—	`conf`	Learning Transferable Policies for Monocular Reactive MAV Control. ISER Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert
2016	J	`jnl`	Learning Transferable Policies for Monocular Reactive MAV Control. CoRR Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert
2016	A*	`conf`	Learning to Filter with Predictive State Inference Machines. ICML Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell
2016	A	`conf`	Learning to Smooth with Bidirectional Predictive State Inference Machines. UAI Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots
2016	A*	`conf`	Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees. IJCAI Wen Sun, J. Andrew Bagnell
2016	A*	`conf`	Online Instrumental Variable Regression with Applications to Online Linear System Identification. AAAI Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots
2016	—	`ch.`	Robot Learning. Springer Handbook of Robotics, 2nd Ed. Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell, Stefan Schaal
2016	J	`jnl`	Robust Monocular Flight in Cluttered Outdoor Environments. CoRR Shreyansh Daftry, Sam Zeng, Arbaaz Khan, Debadeepta Dey, Narek Melik-Barkhudarov, J. Andrew Bagnell, Martial Hebert
2015	—	`conf`	Autonomy Infused Teleoperation with Application to BCI Manipulation. Robotics: Science and Systems Katharina Mülling, Arun Venkatraman, Jean-Sebastien Valois, John Downey, Jeffrey M. Weiss, Shervin Javdani, Martial Hebert, Andrew B. Schwartz, Jennifer L. Collinger, J. Andrew Bagnell
2015	J	`jnl`	Autonomy Infused Teleoperation with Application to BCI Manipulation. CoRR Katharina Mülling, Arun Venkatraman, Jean-Sebastien Valois, John Downey, Jeffrey M. Weiss, Shervin Javdani, Martial Hebert, Andrew B. Schwartz, Jennifer L. Collinger, J. Andrew Bagnell
2015	J	`jnl`	CHIMP, the CMU Highly Intelligent Mobile Platform. J. Field Robotics Anthony Stentz, Herman Herman, Alonzo Kelly, Eric Meyhofer, G. Clark Haynes, David Stager, Brian Zajac, J. Andrew Bagnell, Jordan Brindza, Christopher M. Dellin, Michael David George, Jose Gonzalez-Mora, Sean Hyde, Morgan Jones, Michel Laverne, Maxim Likhachev, Levi Lister, Matthew Powers, Oscar E. Ramos, Justin Ray, David Rice, Justin Scheifflee, Raumi Sidki, Siddhartha S. Srinivasa, Kyle Strabala, Jean-Philippe Tardif, Jean-Sebastien Valois, Michael Vande Weghe, Michael Wagner, Carl Wellington
2015	A*	`conf`	Improving Multi-Step Prediction of Learned Time Series Models. AAAI Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
2015	J	`jnl`	Learning to Filter with Predictive State Inference Machines. CoRR Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell
2015	A*	`conf`	Movement primitives via optimization. ICRA Anca D. Dragan, Katharina Mülling, J. Andrew Bagnell, Siddhartha S. Srinivasa
2015	A	`conf`	Online Bellman Residual Algorithms with Predictive Error Guarantees. UAI Wen Sun, J. Andrew Bagnell
2015	A*	`conf`	Predicting Multiple Structured Visual Interpretations. ICCV Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell
2015	—	`conf`	Shared Autonomy via Hindsight Optimization. Robotics: Science and Systems Shervin Javdani, Siddhartha S. Srinivasa, J. Andrew Bagnell
2015	J	`jnl`	Shared Autonomy via Hindsight Optimization. CoRR Shervin Javdani, J. Andrew Bagnell, Siddhartha S. Srinivasa
2015	A*	`conf`	Submodular Surrogates for Value of Information. AAAI Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew Bagnell, Siddhartha S. Srinivasa, Andreas Krause
2015	—	`conf`	Theoretical Limits of Speed and Resolution for Kinodynamic Planning in a Poisson Forest. Robotics: Science and Systems Sanjiban Choudhury, Sebastian A. Scherer, J. Andrew Bagnell
2015	—	`conf`	Vision and Learning for Deliberative Monocular Cluttered Flight. FSR Debadeepta Dey, Kumar Shaurya Shankar, Sam Zeng, Rupesh Mehta, M. Talha Agcayazi, Christopher Eriksen, Shreyansh Daftry, Martial Hebert, J. Andrew Bagnell
2015	A*	`conf`	Visual chunking: A list prediction framework for region-based object detection. ICRA Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell
2014	J	`jnl`	A Unified View of Large-scale Zero-sum Equilibrium Computation. CoRR Kevin Waugh, J. Andrew Bagnell
2014	J	`jnl`	Efficient Feature Group Sequencing for Anytime Linear Prediction. CoRR Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert
2014	J	`jnl`	Human-inspired force compliant grasping primitives. Auton. Robots Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard
2014	J	`jnl`	Near Optimal Bayesian Active Learning for Decision Making. CoRR Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, J. Andrew Bagnell, Siddhartha S. Srinivasa
2014	J	`jnl`	Perceiving, learning, and exploiting object affordances for autonomous pile manipulation. Auton. Robots Dov Katz, Arun Venkatraman, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz
2014	J	`jnl`	Reinforcement and Imitation Learning via Interactive No-Regret Learning. CoRR Stéphane Ross, J. Andrew Bagnell
2014	J	`jnl`	Solving Games with Functional Regret Estimation. CoRR Kevin Waugh, Dustin Morrill, J. Andrew Bagnell, Michael Bowling
2014	J	`jnl`	Vision and Learning for Deliberative Monocular Cluttered Flight. CoRR Debadeepta Dey, Kumar Shaurya Shankar, Sam Zeng, Rupesh Mehta, M. Talha Agcayazi, Christopher Eriksen, Shreyansh Daftry, Martial Hebert, J. Andrew Bagnell
2014	J	`jnl`	Visual Chunking: A List Prediction Framework for Region-Based Object Detection. CoRR Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell
2013	J	`jnl`	CHOMP: Covariant Hamiltonian optimization for motion planning. Int. J. Robotics Res. Matthew Zucker, Nathan D. Ratliff, Anca D. Dragan, Mihail Pivtoraiko, Matthew Klingensmith, Christopher M. Dellin, J. Andrew Bagnell, Siddhartha S. Srinivasa
2013	A*	`conf`	Clearing a pile of unknown objects using interactive perception. ICRA Dov Katz, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz
2013	J	`jnl`	Computational Rationalization: The Inverse Equilibrium Problem. CoRR Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
2013	A*	`conf`	Efficient 3-D scene analysis from streaming data. ICRA Hanzhang Hu, Daniel Munoz, J. Andrew Bagnell, Martial Hebert
2013	A*	`conf`	Efficient temporal consistency for streaming video scene analysis. ICRA Ondrej Miksik, Daniel Munoz, J. Andrew Bagnell, Martial Hebert
2013	A*	`conf`	Efficient touch based localization through submodularity. ICRA Shervin Javdani, Matthew Klingensmith, J. Andrew Bagnell, Nancy S. Pollard, Siddhartha S. Srinivasa
2013	A*	`conf`	Interactive segmentation, tracking, and kinematic modeling of unknown 3D articulated objects. ICRA Dov Katz, Moslem Kazemi, J. Andrew Bagnell, Anthony Stentz
2013	J	`jnl`	Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization. CoRR Jiaji Zhou, Stéphane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
2013	J	`jnl`	Learning Policies for Contextual Submodular Prediction CoRR Stéphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
2013	A*	`conf`	Learning monocular reactive UAV control in cluttered natural environments. ICRA Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert
2013	J	`jnl`	Learning with Scope, with Application to Information Extraction and Classification CoRR David M. Blei, J. Andrew Bagnell, Andrew McCallum
2013	J	`jnl`	Reinforcement learning in robotics: A survey. Int. J. Robotics Res. Jens Kober, J. Andrew Bagnell, Jan Peters
2013	J	`jnl`	SpeedMachines: Anytime Structured Prediction. CoRR Alexander Grubb, Daniel Munoz, J. Andrew Bagnell, Martial Hebert
2013	J	`jnl`	The Principle of Maximum Causal Entropy for Estimating Interacting Processes. IEEE Trans. Inf. Theory Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
2012	A*	`conf`	Active learning from demonstration for robust autonomous navigation. ICRA David Silver, J. Andrew Bagnell, Anthony Stentz
2012	J	`jnl`	Agnostic System Identification for Model-Based Reinforcement Learning CoRR Stéphane Ross, J. Andrew Bagnell
2012	A	`conf`	An integrated system for autonomous robotics manipulation. IROS J. Andrew Bagnell, Felipe Cavalcanti, Lei Cui, Thomas Galluzzo, Martial Hebert, Moslem Kazemi, Matthew Klingensmith, Jacqueline Libby, Tian Yu Liu, Nancy S. Pollard, Mihail Pivtoraiko, Jean-Sebastien Valois, Ranqi Zhu
2012	—	`conf`	Contextual Sequence Prediction with Application to Control Library Optimization. Robotics: Science and Systems Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell
2012	J	`jnl`	Convex Coding CoRR David M. Bradley, J. Andrew Bagnell
2012	—	`conf`	Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration. ISER David Silver, J. Andrew Bagnell, Anthony Stentz
2012	J	`jnl`	Learning Monocular Reactive UAV Control in Cluttered Natural Environments CoRR Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert
2012	J	`jnl`	Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification CoRR Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
2012	J	`jnl`	Predicting Contextual Sequences via Submodular Function Maximization CoRR Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell
2012	A	`conf`	Probabilistic pointing target prediction via inverse optimal control. IUI Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
2012	A*	`conf`	Reinforcement Planning: RL for optimal planners. ICRA Matthew Zucker, J. Andrew Bagnell
2012	—	`conf`	Robust Object Grasping using Force Compliant Motion Primitives. Robotics: Science and Systems Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard
2011	A*	`conf`	3-D scene analysis via sequenced predictions over points and regions. ICRA Xuehan Xiong, Daniel Munoz, J. Andrew Bagnell, Martial Hebert
2011	J	`jnl`	Anytime online novelty and change detection for mobile robots. J. Field Robotics Boris Sofman, Bradford Neuman, Anthony Stentz, J. Andrew Bagnell
2011	J	`jnl`	Computational Rationalization: The Inverse Equilibrium Problem CoRR Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
2011	J	`jnl`	Generalized Boosting Algorithms for Convex Optimization CoRR Alexander Grubb, J. Andrew Bagnell
2011	A*	`conf`	Learning message-passing inference machines for structured prediction. CVPR Stéphane Ross, Daniel Munoz, Martial Hebert, J. Andrew Bagnell
2011	A	`conf`	Maximum causal entropy correlated equilibria for Markov games. AAMAS Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
2011	J	`jnl`	Optimization and learning for rough terrain legged locomotion. Int. J. Robotics Res. Matthew Zucker, Nathan D. Ratliff, Martin Stolle, Joel E. Chestnutt, J. Andrew Bagnell, Christopher G. Atkeson, James Kuffner
2011	A*	`conf`	Segmentation-based online change detection for mobile robots. ICRA Bradford Neuman, Boris Sofman, Anthony Stentz, J. Andrew Bagnell
2011	J	`jnl`	Stability Conditions for Online Learnability CoRR Stéphane Ross, J. Andrew Bagnell
2010	A*	`conf`	Boosted Backpropagation Learning for Training Deep Modular Networks. ICML Alexander Grubb, J. Andrew Bagnell
2010	J	`jnl`	Learning for Autonomous Navigation. IEEE Robotics Autom. Mag. J. Andrew Bagnell, David M. Bradley, David Silver, Boris Sofman, Anthony Stentz
2010	J	`jnl`	Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain. Int. J. Robotics Res. David Silver, J. Andrew Bagnell, Anthony Stentz
2010	A*	`conf`	Modeling Interaction via the Principle of Maximum Causal Entropy. ICML Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
2010	J	`jnl`	No-Regret Reductions for Imitation Learning and Structured Prediction CoRR Stéphane Ross, Geoffrey J. Gordon, J. Andrew Bagnell
2010	—	`ch.`	Policy Gradient Methods. Encyclopedia of Machine Learning Jan Peters, J. Andrew Bagnell
2010	J	`jnl`	Space-carving Kernels for Accurate Rough Terrain Estimation. Int. J. Robotics Res. Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert
2010	—	`conf`	Stacked Hierarchical Labeling. ECCV (6) Daniel Munoz, J. Andrew Bagnell, Martial Hebert
2009	—	`conf`	Accurate rough terrain estimation with space-carving kernels. Robotics: Science and Systems Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert
2009	—	`conf`	Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain. FSR David Silver, J. Andrew Bagnell, Anthony Stentz
2009	—	`conf`	Bandit-Based Online Candidate Selection for Adjustable Autonomy. FSR Boris Sofman, J. Andrew Bagnell, Anthony Stentz
2009	A*	`conf`	CHOMP: Gradient optimization techniques for efficient motion planning. ICRA Nathan D. Ratliff, Matthew Zucker, J. Andrew Bagnell, Siddhartha S. Srinivasa
2009	A	`conf`	Convex Coding. UAI David M. Bradley, J. Andrew Bagnell
2009	A*	`conf`	GATMO: A Generalized Approach to Tracking Movable Objects. ICRA Garratt Gallagher, Siddhartha S. Srinivasa, J. Andrew Bagnell, Dave Ferguson
2009	—	`conf`	Human Behavior Modeling with Maximum Entropy Inverse Optimal Control. AAAI Spring Symposium: Human Behavior Modeling Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey
2009	A	`conf`	Inverse Optimal Heuristic Control for Imitation Learning. AISTATS Nathan D. Ratliff, Brian D. Ziebart, Kevin M. Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa
2009	J	`jnl`	Learning to search: Functional gradient techniques for imitation learning. Auton. Robots Nathan D. Ratliff, David Silver, J. Andrew Bagnell
2009	C	`conf`	Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning. ISRR David Silver, J. Andrew Bagnell, Anthony Stentz
2008	—	`conf`	Differentiable Sparse Coding. NIPS J. Andrew Bagnell, David M. Bradley
2008	J	`jnl`	Editorial: Special Issue on Machine Learning in Robotics. Int. J. Robotics Res. J. Andrew Bagnell, Stefan Schaal
2008	A*	`conf`	Fast Planning for Dynamic Preferences. ICAPS Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
2008	A*	`conf`	Maximum Entropy Inverse Reinforcement Learning. AAAI Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey
2008	Misc	`conf`	Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. UbiComp Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, J. Andrew Bagnell
2007	A	`conf`	(Approximate) Subgradient Methods for Structured Prediction. AISTATS Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich
2007	A*	`conf`	Kernel Conjugate Gradient for Fast Kernel Machines. IJCAI Nathan D. Ratliff, J. Andrew Bagnell
2007	A*	`conf`	Vegetation Detection for Driving in Complex Environments. ICRA David M. Bradley, Ranjith Unnikrishnan, J. Andrew Bagnell
2006	—	`conf`	Boosting Structured Prediction for Imitation Learning. NIPS Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt
2006	A	`conf`	Experimental Analysis of Overhead Data Processing To Support Long Range Navigation. IROS David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew Bagnell, Anthony Stentz
2006	—	`conf`	Improving Robot Navigation Through Self-Supervised Online Learning. Robotics: Science and Systems Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, Anthony Stentz
2006	J	`jnl`	Improving robot navigation through self-supervised online learning. J. Field Robotics Boris Sofman, Ellie Lin, J. Andrew Bagnell, John Cole, Nicolas Vandapel, Anthony Stentz
2006	A*	`conf`	Maximum margin planning. ICML Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich
2005	—	`conf`	On Local Rewards and Scaling Distributed Reinforcement Learning. NIPS J. Andrew Bagnell, Andrew Y. Ng
2005	A*	`conf`	Robust Supervised Learning. AAAI J. Andrew Bagnell
2003	A*	`conf`	Covariant Policy Search. IJCAI J. Andrew Bagnell, Jeff G. Schneider
2003	—	`conf`	Policy Search by Dynamic Programming. NIPS J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider
2002	A	`conf`	Learning with Scope, with Application to Information Extraction and Classification. UAI David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum
2001	A*	`conf`	Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. ICRA J. Andrew Bagnell, Jeff G. Schneider