Brian D. Ziebart
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
42
Venues
18
Active years
2003–2025
Best venue rank
A*
Where they publish
Papers
42 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | IJCAI | Imitation Learning via Focused Satisficing. | Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski, Ali Farajzadeh, Sanjiban Choudhury, Brian D. Ziebart |
| 2024 | COLING | Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation. | Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Nikolaos Agadakos |
| 2023 | ICML | Superhuman Fairness. | Omid Memarrast, Linh Vu, Brian D. Ziebart |
| 2023 | IROS | Robot Learning to Mop Like Humans Using Video Demonstrations. | Sanket Gaurav, Aaron Crookes, David Hoying, Vignesh Narayanaswamy, Harish Venkataraman, Matthew Barker, Venugopal Vasudevan, Brian D. Ziebart |
| 2023 | PAKDD | Fairness for Robust Learning to Rank. | Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart |
| 2022 | AISTATS | Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. | Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart |
| 2022 | COLING | Towards Enhancing Health Coaching Dialogue in Low-Resource Settings. | Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Ben S. Gerber, Nikolaos Agadakos, Shweta Yadav |
| 2022 | ICML | Towards Uniformly Superhuman Autonomy via Subdominance Minimization. | Brian D. Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza |
| 2021 | AAAI | Robust Fairness Under Covariate Shift. | Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart |
| 2021 | SIGdial | Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches. | Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp |
| 2020 | AAAI | Fairness for Robust Log Loss Classification. | Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart |
| 2020 | FlAIRS | Goal Summarization for Human-Human Health Coaching Dialogues. | Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp |
| 2020 | UAI | Adversarial Learning for 3D Matching. | Wei Xing, Brian D. Ziebart |
| 2020 | SIGdial | Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis. | Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Aiswarya Baiju, Bing Liu, Ben S. Gerber, Lisa K. Sharp, Nadia Nabulsi, Mary Smart |
| 2019 | ICML | Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. | Sima Behpour, Anqi Liu, Brian D. Ziebart |
| 2019 | WACV | ADA: Adversarial Data Augmentation for Object Detection. | Sima Behpour, Kris M. Kitani, Brian D. Ziebart |
| 2018 | AAAI | ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification. | Sima Behpour, Wei Xing, Brian D. Ziebart |
| 2018 | ICML | Efficient and Consistent Adversarial Bipartite Matching. | Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart |
| 2018 | PAKDD | A Game-Theoretic Adversarial Approach to Dynamic Network Prediction. | Jia Li, Brian D. Ziebart, Tanya Y. Berger-Wolf |
| 2017 | ICRA | Goal-predictive robotic teleoperation from noisy sensors. | Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart |
| 2016 | AISTATS | Robust Covariate Shift Regression. | Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart |
| 2016 | IJCAI | Adversarial Sequence Tagging. | Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Y. Berger-Wolf |
| 2016 | UAI | Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. | Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr |
| 2015 | AAAI | A Minimax Robust Approach for Learning to Assist Users with Pointing Tasks. | Sima Behpour, Brian D. Ziebart |
| 2015 | AAAI | Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction. | Anqi Liu, Lev Reyzin, Brian D. Ziebart |
| 2015 | AAAI | Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. | Mathew Monfort, Anqi Liu, Brian D. Ziebart |
| 2015 | AISTATS | Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems. | Xiangli Chen, Brian D. Ziebart |
| 2015 | IJCAI | Graph-Based Inverse Optimal Control for Robot Manipulation. | Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox |
| 2015 | ICTIR | Context Retrieval for Web Tables. | Hong Wang, Anqi Liu, Jing Wang, Brian D. Ziebart, Clement T. Yu, Warren Shen |
| 2015 | UAI | Adversarial Cost-Sensitive Classification. | Kaiser Asif, Wei Xing, Sima Behpour, Brian D. Ziebart |
| 2014 | CCS | Leveraging Machine Learning to Improve Unwanted Resource Filtering. | Sruti Bhagavatula, Christopher W. Dunn, Chris Kanich, Minaxi Gupta, Brian D. Ziebart |
| 2012 | ECCV | Activity Forecasting. | Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert |
| 2012 | IUI | Probabilistic pointing target prediction via inverse optimal control. | Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell |
| 2011 | CHI | Learning patterns of pick-ups and drop-offs to support busy family coordination. | Scott Davidoff, Brian D. Ziebart, John Zimmerman, Anind K. Dey |
| 2011 | ICML | Computational Rationalization: The Inverse Equilibrium Problem. | Kevin Waugh, Brian D. Ziebart, Drew Bagnell |
| 2010 | AAAI | Maximum Causal Entropy Correlated Equilibria for Markov Games. | Brian D. Ziebart, Drew Bagnell, Anind K. Dey |
| 2010 | ICML | Modeling Interaction via the Principle of Maximum Causal Entropy. | Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey |
| 2009 | IROS | Planning-based prediction for pedestrians. | 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 | AAAI | Maximum Entropy Inverse Reinforcement Learning. | Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey |
| 2007 | UAI | Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. | Brian D. Ziebart, Anind K. Dey, James A. Bagnell |
| 2005 | PERCOM | Towards a Pervasive Computing Benchmark. | Anand Ranganathan, Jalal Al-Muhtadi, Jacob T. Biehl, Brian D. Ziebart, Roy H. Campbell, Brian P. Bailey |
| 2003 | PERCOM | Dynamic Application Composition: Customizing the Behavior of an Active Space. | Manuel Romn, Brian D. Ziebart, Roy H. Campbell |