Brian D. Ziebart

94 papers A* 24A 14B 3Misc 4Journal 23Unranked 25

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Imitation Learning via Focused Satisficing. IJCAI Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski, Ali Farajzadeh, Sanjiban Choudhury, Brian D. Ziebart
2025	J	`jnl`	Imitation Learning via Focused Satisficing. CoRR Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski, Ali Farajzadeh, Sanjiban Choudhury, Brian D. Ziebart
2024	—	`conf`	Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation. LREC/COLING Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Nikolaos Agadakos
2024	J	`jnl`	Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation. CoRR Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Nikolaos Agadakos
2024	J	`jnl`	Towards Enhancing Health Coaching Dialogue in Low-Resource Settings. CoRR Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Ben S. Gerber, Nikolaos Agadakos, Shweta Yadav
2023	A*	`conf`	Distributionally Robust Skeleton Learning of Discrete Bayesian Networks. NeurIPS Yeshu Li, Brian D. Ziebart
2023	J	`jnl`	Distributionally Robust Skeleton Learning of Discrete Bayesian Networks. CoRR Yeshu Li, Brian D. Ziebart
2023	—	`conf`	Fairness for Robust Learning to Rank. PAKDD (1) Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart
2023	A	`conf`	Robot Learning to Mop Like Humans Using Video Demonstrations. IROS Sanket Gaurav, Aaron Crookes, David Hoying, Vignesh Narayanaswamy, Harish Venkataraman, Matthew Barker, Venugopal Vasudevan, Brian D. Ziebart
2023	A*	`conf`	Superhuman Fairness. ICML Omid Memarrast, Linh Vu, Brian D. Ziebart
2023	J	`jnl`	Superhuman Fairness. CoRR Omid Memarrast, Linh Vu, Brian D. Ziebart
2022	A	`conf`	Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. AISTATS Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart
2022	J	`jnl`	Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs. J. Intell. Manuf. Kaiser Asif, Lu Zhang, Sybil Derrible, J. Ernesto Indacochea, Didem Ozevin, Brian D. Ziebart
2022	A*	`conf`	Moment Distributionally Robust Tree Structured Prediction. NeurIPS Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian D. Ziebart, Kevin Gimpel
2022	J	`jnl`	Risk-averse policy optimization via risk-neutral policy optimization. Artif. Intell. Lorenzo Bisi, Davide Santambrogio, Federico Sandrelli, Andrea Tirinzoni, Brian D. Ziebart, Marcello Restelli
2022	B	`conf`	Towards Enhancing Health Coaching Dialogue in Low-Resource Settings. COLING Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Ben S. Gerber, Nikolaos Agadakos, Shweta Yadav
2022	A*	`conf`	Towards Uniformly Superhuman Autonomy via Subdominance Minimization. ICML Brian D. Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza
2021	A*	`conf`	Distributionally Robust Imitation Learning. NeurIPS Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang
2021	J	`jnl`	Fairness for Robust Learning to Rank. CoRR Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart
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`	Robust Fairness Under Covariate Shift. AAAI Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart
2021	B	`conf`	Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches. SIGDIAL Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp
2020	A	`conf`	Adversarial Learning for 3D Matching. UAI Wei Xing, Brian D. Ziebart
2020	A*	`conf`	Fairness for Robust Log Loss Classification. AAAI Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart
2020	Misc	`conf`	Goal Summarization for Human-Human Health Coaching Dialogues. FLAIRS Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp
2020	B	`conf`	Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis. SIGdial Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Aiswarya Baiju, Bing Liu, Ben S. Gerber, Lisa K. Sharp, Nadia Nabulsi, Mary Smart
2020	—	`conf`	Recurrent Neural Networks for Hierarchically Mapping Human-Robot Poses. IRC Zainab Al-Qurashi, Brian D. Ziebart
2020	J	`jnl`	Robust Fairness under Covariate Shift. CoRR Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart
2019	A	`conf`	ADA: Adversarial Data Augmentation for Object Detection. WACV Sima Behpour, Kris M. Kitani, Brian D. Ziebart
2019	A*	`conf`	Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. ICML Sima Behpour, Anqi Liu, Brian D. Ziebart
2019	—	`conf`	Deep Correspondence Learning for Effective Robotic Teleoperation using Virtual Reality. Humanoids Sanket Gaurav, Zainab Al-Qurashi, Amey Barapatre, George Maratos, Tejas Sarma, Brian D. Ziebart
2019	A	`conf`	Discriminatively Learning Inverse Optimal Control Models for Predicting Human Intentions. AAMAS Sanket Gaurav, Brian D. Ziebart
2019	J	`jnl`	Fair Logistic Regression: An Adversarial Perspective. CoRR Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart
2019	—	`conf`	Hybrid Algorithm for Inverse Kinematics Using Deep Learning and Coordinate Transformation. IRC Zainab Al-Qurashi, Brian D. Ziebart
2019	—	`conf`	Modeling Health Coaching Dialogues for Behavioral Goal Extraction. BIBM Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp
2018	—	`conf`	A Game-Theoretic Adversarial Approach to Dynamic Network Prediction. PAKDD (3) Jia Li, Brian D. Ziebart, Tanya Y. Berger-Wolf
2018	A*	`conf`	ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification. AAAI Sima Behpour, Wei Xing, Brian D. Ziebart
2018	J	`jnl`	Consistent Robust Adversarial Prediction for General Multiclass Classification. CoRR Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
2018	A*	`conf`	Distributionally Robust Graphical Models. NeurIPS Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
2018	J	`jnl`	Distributionally Robust Graphical Models. CoRR Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
2018	A*	`conf`	Efficient and Consistent Adversarial Bipartite Matching. ICML Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
2018	A*	`conf`	Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. NeurIPS Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian D. Ziebart
2018	—	`conf`	Towards Building a Virtual Assistant Health Coach. ICHI Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp, Rafe Davis, Aiswarya Baiju
2017	J	`jnl`	Adversarial Structured Prediction for Multivariate Measures. CoRR Hong Wang, Ashkan Rezaei, Brian D. Ziebart
2017	—	`conf`	Adversarial Surrogate Losses for Ordinal Regression. NIPS Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart
2017	J	`jnl`	Adversarially Optimizing Intersection over Union for Object Localization Tasks. CoRR Sima Behpour, Kris M. Kitani, Brian D. Ziebart
2017	A*	`conf`	Goal-predictive robotic teleoperation from noisy sensors. ICRA Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart
2017	J	`jnl`	Kernel Robust Bias-Aware Prediction under Covariate Shift. CoRR Anqi Liu, Rizal Fathony, Brian D. Ziebart
2017	J	`jnl`	Robust Covariate Shift Prediction with General Losses and Feature Views. CoRR Anqi Liu, Brian D. Ziebart
2016	A	`conf`	Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. UAI Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr
2016	—	`conf`	Adversarial Multiclass Classification: A Risk Minimization Perspective. NIPS Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart
2016	A*	`conf`	Adversarial Sequence Tagging. IJCAI Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Y. Berger-Wolf
2016	A	`conf`	Robust Covariate Shift Regression. AISTATS Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart
2015	—	`conf`	A Minimax Robust Approach for Learning to Assist Users with Pointing Tasks. AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments Sima Behpour, Brian D. Ziebart
2015	J	`jnl`	A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment. Trans. Assoc. Comput. Linguistics Jing Wang, Mohit Bansal, Kevin Gimpel, Brian D. Ziebart, Clement T. Yu
2015	A	`conf`	Adversarial Cost-Sensitive Classification. UAI Kaiser Asif, Wei Xing, Sima Behpour, Brian D. Ziebart
2015	—	`conf`	Adversarial Prediction Games for Multivariate Losses. NIPS Hong Wang, Wei Xing, Kaiser Asif, Brian D. Ziebart
2015	Misc	`conf`	Context Retrieval for Web Tables. ICTIR Hong Wang, Anqi Liu, Jing Wang, Brian D. Ziebart, Clement T. Yu, Warren Shen
2015	A*	`conf`	Graph-Based Inverse Optimal Control for Robot Manipulation. IJCAI Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox
2015	A*	`conf`	Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. AAAI Mathew Monfort, Anqi Liu, Brian D. Ziebart
2015	A	`conf`	Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems. AISTATS Xiangli Chen, Brian D. Ziebart
2015	J	`jnl`	Serendipity-empowered path planning for predictive task completion. J. Ambient Intell. Smart Environ. Choonsung Shin, Brian D. Ziebart, Anind K. Dey
2015	A*	`conf`	Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction. AAAI Anqi Liu, Lev Reyzin, Brian D. Ziebart
2015	—	`conf`	Softstar: Heuristic-Guided Probabilistic Inference. NIPS Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum
2014	—	`conf`	3 UbiComp Adjunct Klaus David, Stephan Sigg, Rico Kusber, Brian D. Ziebart, Sian Lun Lau
2014	—	`conf`	Leveraging Machine Learning to Improve Unwanted Resource Filtering. AISec@CCS Sruti Bhagavatula, Christopher W. Dunn, Chris Kanich, Minaxi Gupta, Brian D. Ziebart
2014	—	`conf`	Robust Classification Under Sample Selection Bias. NIPS Anqi Liu, Brian D. Ziebart
2013	—	`conf`	2nd workshop on recent advances in behavior prediction and pro-active pervasive computing. UbiComp (Adjunct Publication) Klaus David, Bernd Niklas Klein, Sian Lun Lau, Stephan Sigg, Brian D. Ziebart
2013	J	`jnl`	Computational Rationalization: The Inverse Equilibrium Problem. CoRR Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
2013	—	`conf`	Robust structure estimation of maximum causal entropy processes. Allerton Brian D. Ziebart
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
2013	Misc	`conf`	TherML: occupancy prediction for thermostat control. UbiComp Christian Koehler, Brian D. Ziebart, Jennifer Mankoff, Anind K. Dey
2012	—	`conf`	Activity Forecasting. ECCV (4) Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert
2012	—	`conf`	Identifying Culture and Leveraging Cultural Differences for Negotiation Agents. HICSS Brian D. Ziebart, Miroslav Dudík, Geoffrey J. Gordon, Katia P. Sycara, Wendi L. Adair, Jeanne M. Brett
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	A	`conf`	Probabilistic pointing target prediction via inverse optimal control. IUI Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
2011	J	`jnl`	Computational Rationalization: The Inverse Equilibrium Problem CoRR Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
2011	A*	`conf`	Computational Rationalization: The Inverse Equilibrium Problem. ICML Kevin Waugh, Brian D. Ziebart, Drew Bagnell
2011	—	`conf`	Factorized decision forecasting via combining value-based and reward-based estimation. Allerton Brian D. Ziebart
2011	A*	`conf`	Learning patterns of pick-ups and drop-offs to support busy family coordination. CHI Scott Davidoff, Brian D. Ziebart, John Zimmerman, Anind K. Dey
2011	A	`conf`	Maximum causal entropy correlated equilibria for Markov games. AAMAS Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
2010	—	`conf`	Maximum Causal Entropy Correlated Equilibria for Markov Games. Interactive Decision Theory and Game Theory Brian D. Ziebart, Drew Bagnell, Anind K. Dey
2010	A*	`conf`	Modeling Interaction via the Principle of Maximum Causal Entropy. ICML Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
2010	—	`—`	Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy. Brian D. Ziebart
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	A	`conf`	Planning-based prediction for pedestrians. IROS Brian D. Ziebart, Nathan D. Ratliff, Garratt Gallagher, Christoph Mertz, Kevin M. Peterson, James A. Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa
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`	Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. UAI Brian D. Ziebart, Anind K. Dey, James A. Bagnell
2005	—	`conf`	Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management. ICAC Brian D. Ziebart, Dan Roth, Roy H. Campbell, Anind K. Dey
2005	—	`conf`	Towards a Pervasive Computing Benchmark. PerCom Workshops Anand Ranganathan, Jalal Al-Muhtadi, Jacob T. Biehl, Brian D. Ziebart, Roy H. Campbell, Brian P. Bailey
2003	A*	`conf`	Dynamic Application Composition: Customizing the Behavior of an Active Space. PerCom Manuel Román, Brian D. Ziebart, Roy H. Campbell