J. Andrew Bagnell

Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey: The Principle of Maximum Causal Entropy for Estimating Interacting Processes. IEEE Transactions on Information Theory 59(4): 1966-1980 (2013)

David M. Blei, J. Andrew Bagnell, Andrew McCallum: Learning with Scope, with Application to Information Extraction and Classification. CoRR abs/1301.0556 (2013)

Alexander Grubb, Drew Bagnell: SpeedBoost: Anytime Prediction with Uniform Near-Optimality. Journal of Machine Learning Research - Proceedings Track 22: 458-466 (2012)

Debadeepta Dey, Tian Yu Liu, Boris Sofman, James Andrew Bagnell: Efficient Optimization of Control Libraries. AAAI 2012

Yuichi Ito, Kris M. Kitani, James A. Bagnell, Martial Hebert: Detecting Interesting Events Using Unsupervised Density Ratio Estimation. ECCV Workshops (3) 2012: 151-161

Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert: Activity Forecasting. ECCV (4) 2012: 201-214

Daniel Munoz, James Andrew Bagnell, Martial Hebert: Co-inference for Multi-modal Scene Analysis. ECCV (6) 2012: 668-681

Stéphane Ross, Drew Bagnell: Agnostic System Identification for Model-Based Reinforcement Learning. ICML 2012

David Silver, J. Andrew Bagnell, Anthony Stentz: Active learning from demonstration for robust autonomous navigation. ICRA 2012: 200-207

Matthew Zucker, J. Andrew Bagnell: Reinforcement Planning: RL for optimal planners. ICRA 2012: 1850-1855

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: An integrated system for autonomous robotics manipulation. IROS 2012: 2955-2962

Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell: Probabilistic pointing target prediction via inverse optimal control. IUI 2012: 1-10

Paul Vernaza, Drew Bagnell: Efficient high dimensional maximum entropy modeling via symmetric partition functions. NIPS 2012: 584-592

Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell: Contextual Sequence Prediction with Application to Control Library Optimization. Robotics: Science and Systems 2012

Moslem Kazemi, Jean-Sebastien Valois, J. Andrew Bagnell, Nancy S. Pollard: Robust Object Grasping using Force Compliant Motion Primitives. Robotics: Science and Systems 2012

Debadeepta Dey, Tian Yu Liu, Martial Hebert, J. Andrew Bagnell: Predicting Contextual Sequences via Submodular Function Maximization. CoRR abs/1202.2112 (2012)

Stéphane Ross, J. Andrew Bagnell: Agnostic System Identification for Model-Based Reinforcement Learning. CoRR abs/1203.1007 (2012)

David M. Bradley, J. Andrew Bagnell: Convex Coding. CoRR abs/1205.2656 (2012)

Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell: Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. CoRR abs/1206.5281 (2012)

Shervin Javdani, Matthew Klingensmith, Drew Bagnell, Nancy S. Pollard, Siddhartha S. Srinivasa: Efficient Touch Based Localization through Submodularity. CoRR abs/1208.6067 (2012)

Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert: Learning Monocular Reactive UAV Control in Cluttered Natural Environments. CoRR abs/1211.1690 (2012)

Matthew Zucker, Nathan D. Ratliff, Martin Stolle, Joel E. Chestnutt, J. Andrew Bagnell, Christopher G. Atkeson, James Kuffner: Optimization and learning for rough terrain legged locomotion. I. J. Robotic Res. 30(2): 175-191 (2011)

Boris Sofman, Bradford Neuman, Anthony Stentz, J. Andrew Bagnell: Anytime online novelty and change detection for mobile robots. J. Field Robotics 28(4): 589-618 (2011)

Stéphane Ross, Geoffrey J. Gordon, Drew Bagnell: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. Journal of Machine Learning Research - Proceedings Track 15: 627-635 (2011)

Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey: Maximum causal entropy correlated equilibria for Markov games. AAMAS 2011: 207-214

Stéphane Ross, Daniel Munoz, Martial Hebert, J. Andrew Bagnell: Learning message-passing inference machines for structured prediction. CVPR 2011: 2737-2744

Kevin Waugh, Brian D. Ziebart, Drew Bagnell: Computational Rationalization: The Inverse Equilibrium Problem. ICML 2011: 1169-1176

Alexander Grubb, Drew Bagnell: Generalized Boosting Algorithms for Convex Optimization. ICML 2011: 1209-1216

Xuehan Xiong, Daniel Munoz, J. Andrew Bagnell, Martial Hebert: 3-D scene analysis via sequenced predictions over points and regions. ICRA 2011: 2609-2616

Bradford Neuman, Boris Sofman, Anthony Stentz, J. Andrew Bagnell: Segmentation-based online change detection for mobile robots. ICRA 2011: 5427-5434

Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell: Computational Rationalization: The Inverse Equilibrium Problem. CoRR abs/1103.5254 (2011)

Alexander Grubb, J. Andrew Bagnell: Generalized Boosting Algorithms for Convex Optimization. CoRR abs/1105.2054 (2011)

Stéphane Ross, J. Andrew Bagnell: Stability Conditions for Online Learnability. CoRR abs/1108.3154 (2011)

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert: Space-carving Kernels for Accurate Rough Terrain Estimation. I. J. Robotic Res. 29(8): 981-996 (2010)

David Silver, J. Andrew Bagnell, Anthony Stentz: Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain. I. J. Robotic Res. 29(12): 1565-1592 (2010)

Stéphane Ross, Drew Bagnell: Efficient Reductions for Imitation Learning. Journal of Machine Learning Research - Proceedings Track 9: 661-668 (2010)

J. Andrew Bagnell, David M. Bradley, David Silver, Boris Sofman, Anthony Stentz: Learning for Autonomous Navigation. IEEE Robot. Automat. Mag. 17(2): 74-84 (2010)

Brian D. Ziebart, Drew Bagnell, Anind K. Dey: Maximum Causal Entropy Correlated Equilibria for Markov Games. Interactive Decision Theory and Game Theory 2010

Daniel Munoz, J. Andrew Bagnell, Martial Hebert: Stacked Hierarchical Labeling. ECCV (6) 2010: 57-70

Alexander Grubb, J. Andrew Bagnell: Boosted Backpropagation Learning for Training Deep Modular Networks. ICML 2010: 407-414

Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey: Modeling Interaction via the Principle of Maximum Causal Entropy. ICML 2010: 1255-1262

Boris Sofman, James A. Bagnell, Anthony Stentz: Anytime online novelty detection for vehicle safeguarding. ICRA 2010: 1247-1254

Matthew Zucker, James A. Bagnell, Christopher G. Atkeson, James Kuffner: An optimization approach to rough terrain locomotion. ICRA 2010: 3589-3595

Jan Peters, J. Andrew Bagnell: Policy Gradient Methods. Encyclopedia of Machine Learning 2010: 774-776

Stéphane Ross, Geoffrey J. Gordon, J. Andrew Bagnell: No-Regret Reductions for Imitation Learning and Structured Prediction. CoRR abs/1011.0686 (2010)

Nathan D. Ratliff, David Silver, J. Andrew Bagnell: Learning to search: Functional gradient techniques for imitation learning. Auton. Robots 27(1): 25-53 (2009)

Nathan D. Ratliff, Brian D. Ziebart, Kevin Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa: Inverse Optimal Heuristic Control for Imitation Learning. Journal of Machine Learning Research - Proceedings Track 5: 424-431 (2009)

Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey: Human Behavior Modeling with Maximum Entropy Inverse Optimal Control. AAAI Spring Symposium: Human Behavior Modeling 2009: 92-

Daniel Munoz, James A. Bagnell, Nicolas Vandapel, Martial Hebert: Contextual classification with functional Max-Margin Markov Networks. CVPR 2009: 975-982

Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher R. Baker, Robert Bittner, M. N. Clark, John M. Dolan, Dave Duggins, Tugrul Galatali, Christopher Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matthew McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul E. Rybski, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod M. Snider, Anthony Stentz, William Whittaker, Ziv Wolkowicki, Jason Ziglar, Hong Bae, Thomas Brown, Daniel Demitrish, Bakhtiar Litkouhi, Jim Nickolaou, Varsha Sadekar, Wende Zhang, Joshua Struble, Michael Taylor, Michael Darms, Dave Ferguson: Autonomous Driving in Urban Environments: Boss and the Urban Challenge. The DARPA Urban Challenge 2009: 1-59

Boris Sofman, J. Andrew Bagnell, Anthony Stentz: Bandit-Based Online Candidate Selection for Adjustable Autonomy. FSR 2009: 239-248

David Silver, J. Andrew Bagnell, Anthony Stentz: Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain. FSR 2009: 249-259

Nathan D. Ratliff, Matthew Zucker, J. Andrew Bagnell, Siddhartha S. Srinivasa: CHOMP: Gradient optimization techniques for efficient motion planning. ICRA 2009: 489-494

Garratt Gallagher, Siddhartha S. Srinivasa, J. Andrew Bagnell, Dave Ferguson: GATMO: A Generalized Approach to Tracking Movable Objects. ICRA 2009: 2043-2048

Brian D. Ziebart, Nathan D. Ratliff, Garratt Gallagher, Christoph Mertz, Kevin M. Peterson, James A. Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa: Planning-based prediction for pedestrians. IROS 2009: 3931-3936

David Silver, J. Andrew Bagnell, Anthony Stentz: Perceptual Interpretation for Autonomous Navigation through Dynamic Imitation Learning. ISRR 2009: 433-449

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert: Accurate rough terrain estimation with space-carving kernels. Robotics: Science and Systems 2009

J. Andrew Bagnell, Stefan Schaal: Editorial: Special Issue on Machine Learning in Robotics. I. J. Robotic Res. 27(2): 155-156 (2008)

Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher R. Baker, Robert Bittner, M. N. Clark, John M. Dolan, Dave Duggins, Tugrul Galatali, Christopher Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matthew McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul E. Rybski, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod M. Snider, Anthony Stentz, William Whittaker, Ziv Wolkowicki, Jason Ziglar, Hong Bae, Thomas Brown, Daniel Demitrish, Bakhtiar Litkouhi, Jim Nickolaou, Varsha Sadekar, Wende Zhang, Joshua Struble, Michael Taylor, Michael Darms, Dave Ferguson: Autonomous driving in urban environments: Boss and the Urban Challenge. J. Field Robotics 25(8): 425-466 (2008)

Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey: Maximum Entropy Inverse Reinforcement Learning. AAAI 2008: 1433-1438

Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell: Fast Planning for Dynamic Preferences. ICAPS 2008: 412-419

Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, J. Andrew Bagnell: Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. UbiComp 2008: 322-331

Matthew Zucker, James Kuffner, James A. Bagnell: Adaptive workspace biasing for sampling-based planners. ICRA 2008: 3757-3762

J. Andrew Bagnell, David M. Bradley: Differentiable Sparse Coding. NIPS 2008: 113-120

David Silver, James A. Bagnell, Anthony Stentz: High Performance Outdoor Navigation from Overhead Data using Imitation Learning. Robotics: Science and Systems 2008

Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich: (Approximate) Subgradient Methods for Structured Prediction. Journal of Machine Learning Research - Proceedings Track 2: 380-387 (2007)

Nathan D. Ratliff, James A. Bagnell, Siddhartha S. Srinivasa: Imitation learning for locomotion and manipulation. Humanoids 2007: 392-397

Nathan D. Ratliff, J. Andrew Bagnell: Kernel Conjugate Gradient for Fast Kernel Machines. IJCAI 2007: 1017-1022

Brian D. Ziebart, Anind K. Dey, James A. Bagnell: Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. UAI 2007: 458-465

Boris Sofman, Ellie Lin, J. Andrew Bagnell, John Cole, Nicolas Vandapel, Anthony Stentz: Improving robot navigation through self-supervised online learning. J. Field Robotics 23(11-12): 1059-1075 (2006)

Nathan D. Ratliff, J. Andrew Bagnell, Martin Zinkevich: Maximum margin planning. ICML 2006: 729-736

David Silver, Boris Sofman, Nicolas Vandapel, J. Andrew Bagnell, Anthony Stentz: Experimental Analysis of Overhead Data Processing To Support Long Range Navigation. IROS 2006: 2443-2450

Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt: Boosting Structured Prediction for Imitation Learning. NIPS 2006: 1153-1160

Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, Anthony Stentz: Improving Robot Navigation Through Self-Supervised Online Learning. Robotics: Science and Systems 2006

J. Andrew Bagnell: Robust Supervised Learning. AAAI 2005: 714-719

Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons: Learning Opportunity Costs in Multi-Robot Market Based Planners. ICRA 2005: 1151-1156

J. Andrew Bagnell, Andrew Y. Ng: On Local Rewards and Scaling Distributed Reinforcement Learning. NIPS 2005

J. Andrew Bagnell, Jeff G. Schneider: Covariant Policy Search. IJCAI 2003: 1019-1024

J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff G. Schneider: Policy Search by Dynamic Programming. NIPS 2003

David M. Blei, J. Andrew Bagnell, Andrew McCallum: Learning with Scope, with Application to Information Extraction and Classification. UAI 2002: 53-60

J. Andrew Bagnell, Jeff G. Schneider: Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. ICRA 2001: 1615-1620