default search action
Brian D. Ziebart
Person information
- affiliation: University of Illinois at Chicago, Department of Computer Science
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [c69]Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Nikolaos Agadakos:
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation. LREC/COLING 2024: 11498-11509 - [i17]Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Ben S. Gerber, Nikolaos Agadakos, Shweta Yadav:
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings. CoRR abs/2404.08888 (2024) - [i16]Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Nikolaos Agadakos:
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation. CoRR abs/2404.10268 (2024) - 2023
- [c68]Omid Memarrast, Linh Vu, Brian D. Ziebart:
Superhuman Fairness. ICML 2023: 24420-24435 - [c67]Sanket Gaurav, Aaron Crookes, David Hoying, Vignesh Narayanaswamy, Harish Venkataraman, Matthew Barker, Venugopal Vasudevan, Brian D. Ziebart:
Robot Learning to Mop Like Humans Using Video Demonstrations. IROS 2023: 9947-9954 - [c66]Yeshu Li, Brian D. Ziebart:
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks. NeurIPS 2023 - [c65]Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart:
Fairness for Robust Learning to Rank. PAKDD (1) 2023: 544-556 - [i15]Omid Memarrast, Linh Vu, Brian D. Ziebart:
Superhuman Fairness. CoRR abs/2301.13420 (2023) - [i14]Yeshu Li, Brian D. Ziebart:
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks. CoRR abs/2311.06117 (2023) - 2022
- [j5]Lorenzo Bisi, Davide Santambrogio, Federico Sandrelli, Andrea Tirinzoni, Brian D. Ziebart, Marcello Restelli:
Risk-averse policy optimization via risk-neutral policy optimization. Artif. Intell. 311: 103765 (2022) - [j4]Kaiser Asif, Lu Zhang, Sybil Derrible, J. Ernesto Indacochea, Didem Ozevin, Brian D. Ziebart:
Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs. J. Intell. Manuf. 33(3): 881-895 (2022) - [c64]Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. AISTATS 2022: 8997-9016 - [c63]Yue Zhou, Barbara Di Eugenio, Brian D. Ziebart, Lisa K. Sharp, Bing Liu, Ben S. Gerber, Nikolaos Agadakos, Shweta Yadav:
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings. COLING 2022: 694-706 - [c62]Brian D. Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza:
Towards Uniformly Superhuman Autonomy via Subdominance Minimization. ICML 2022: 27654-27670 - [c61]Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian D. Ziebart, Kevin Gimpel:
Moment Distributionally Robust Tree Structured Prediction. NeurIPS 2022 - 2021
- [c60]Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart:
Robust Fairness Under Covariate Shift. AAAI 2021: 9419-9427 - [c59]Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang:
Distributionally Robust Imitation Learning. NeurIPS 2021: 24404-24417 - [c58]Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp:
Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches. SIGDIAL 2021: 276-289 - [i13]Jonathan C. Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian D. Ziebart, J. Andrew Bagnell:
Feedback in Imitation Learning: The Three Regimes of Covariate Shift. CoRR abs/2102.02872 (2021) - [i12]Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart:
Fairness for Robust Learning to Rank. CoRR abs/2112.06288 (2021) - 2020
- [c57]Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart:
Fairness for Robust Log Loss Classification. AAAI 2020: 5511-5518 - [c56]Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp:
Goal Summarization for Human-Human Health Coaching Dialogues. FLAIRS 2020: 317-322 - [c55]Zainab Al-Qurashi, Brian D. Ziebart:
Recurrent Neural Networks for Hierarchically Mapping Human-Robot Poses. IRC 2020: 63-70 - [c54]Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Aiswarya Baiju, Bing Liu, Ben S. Gerber, Lisa K. Sharp, Nadia Nabulsi, Mary Smart:
Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis. SIGdial 2020: 246-256 - [c53]Wei Xing, Brian D. Ziebart:
Adversarial Learning for 3D Matching. UAI 2020: 869-878 - [i11]Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart:
Robust Fairness under Covariate Shift. CoRR abs/2010.05166 (2020)
2010 – 2019
- 2019
- [c52]Sanket Gaurav, Brian D. Ziebart:
Discriminatively Learning Inverse Optimal Control Models for Predicting Human Intentions. AAMAS 2019: 1368-1376 - [c51]Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp:
Modeling Health Coaching Dialogues for Behavioral Goal Extraction. BIBM 2019: 1188-1190 - [c50]Sanket Gaurav, Zainab Al-Qurashi, Amey Barapatre, George Maratos, Tejas Sarma, Brian D. Ziebart:
Deep Correspondence Learning for Effective Robotic Teleoperation using Virtual Reality. Humanoids 2019: 477-483 - [c49]Sima Behpour, Anqi Liu, Brian D. Ziebart:
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. ICML 2019: 563-572 - [c48]Zainab Al-Qurashi, Brian D. Ziebart:
Hybrid Algorithm for Inverse Kinematics Using Deep Learning and Coordinate Transformation. IRC 2019: 377-380 - [c47]Sima Behpour, Kris M. Kitani, Brian D. Ziebart:
ADA: Adversarial Data Augmentation for Object Detection. WACV 2019: 1243-1252 - [i10]Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart:
Fair Logistic Regression: An Adversarial Perspective. CoRR abs/1903.03910 (2019) - 2018
- [c46]Sima Behpour, Wei Xing, Brian D. Ziebart:
ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification. AAAI 2018: 2704-2711 - [c45]Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp, Rafe Davis, Aiswarya Baiju:
Towards Building a Virtual Assistant Health Coach. ICHI 2018: 419-421 - [c44]Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart:
Efficient and Consistent Adversarial Bipartite Matching. ICML 2018: 1456-1465 - [c43]Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Graphical Models. NeurIPS 2018: 8354-8365 - [c42]Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian D. Ziebart:
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. NeurIPS 2018: 8953-8963 - [c41]Jia Li, Brian D. Ziebart, Tanya Y. Berger-Wolf:
A Game-Theoretic Adversarial Approach to Dynamic Network Prediction. PAKDD (3) 2018: 677-688 - [i9]Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart:
Distributionally Robust Graphical Models. CoRR abs/1811.02728 (2018) - [i8]Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart:
Consistent Robust Adversarial Prediction for General Multiclass Classification. CoRR abs/1812.07526 (2018) - 2017
- [c40]Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart:
Goal-predictive robotic teleoperation from noisy sensors. ICRA 2017: 5377-5383 - [c39]Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart:
Adversarial Surrogate Losses for Ordinal Regression. NIPS 2017: 563-573 - [i7]Sima Behpour, Kris M. Kitani, Brian D. Ziebart:
Adversarially Optimizing Intersection over Union for Object Localization Tasks. CoRR abs/1710.07735 (2017) - [i6]Hong Wang, Ashkan Rezaei, Brian D. Ziebart:
Adversarial Structured Prediction for Multivariate Measures. CoRR abs/1712.07374 (2017) - [i5]Anqi Liu, Brian D. Ziebart:
Robust Covariate Shift Prediction with General Losses and Feature Views. CoRR abs/1712.10043 (2017) - [i4]Anqi Liu, Rizal Fathony, Brian D. Ziebart:
Kernel Robust Bias-Aware Prediction under Covariate Shift. CoRR abs/1712.10050 (2017) - 2016
- [c38]Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart:
Robust Covariate Shift Regression. AISTATS 2016: 1270-1279 - [c37]Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Y. Berger-Wolf:
Adversarial Sequence Tagging. IJCAI 2016: 1690-1696 - [c36]Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart:
Adversarial Multiclass Classification: A Risk Minimization Perspective. NIPS 2016: 559-567 - [c35]Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr:
Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. UAI 2016 - 2015
- [j3]Choonsung Shin, Brian D. Ziebart, Anind K. Dey:
Serendipity-empowered path planning for predictive task completion. J. Ambient Intell. Smart Environ. 7(5): 605-616 (2015) - [j2]Jing Wang, Mohit Bansal, Kevin Gimpel, Brian D. Ziebart, Clement T. Yu:
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment. Trans. Assoc. Comput. Linguistics 3: 59-71 (2015) - [c34]Sima Behpour, Brian D. Ziebart:
A Minimax Robust Approach for Learning to Assist Users with Pointing Tasks. AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments 2015 - [c33]Anqi Liu, Lev Reyzin, Brian D. Ziebart:
Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction. AAAI 2015: 2764-2770 - [c32]Mathew Monfort, Anqi Liu, Brian D. Ziebart:
Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. AAAI 2015: 3672-3678 - [c31]Xiangli Chen, Brian D. Ziebart:
Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems. AISTATS 2015 - [c30]Hong Wang, Anqi Liu, Jing Wang, Brian D. Ziebart, Clement T. Yu, Warren Shen:
Context Retrieval for Web Tables. ICTIR 2015: 251-260 - [c29]Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox:
Graph-Based Inverse Optimal Control for Robot Manipulation. IJCAI 2015: 1874-1880 - [c28]Hong Wang, Wei Xing, Kaiser Asif, Brian D. Ziebart:
Adversarial Prediction Games for Multivariate Losses. NIPS 2015: 2728-2736 - [c27]Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum:
Softstar: Heuristic-Guided Probabilistic Inference. NIPS 2015: 2764-2772 - [c26]Kaiser Asif, Wei Xing, Sima Behpour, Brian D. Ziebart:
Adversarial Cost-Sensitive Classification. UAI 2015: 92-101 - 2014
- [c25]Sruti Bhagavatula, Christopher W. Dunn, Chris Kanich, Minaxi Gupta, Brian D. Ziebart:
Leveraging Machine Learning to Improve Unwanted Resource Filtering. AISec@CCS 2014: 95-102 - [c24]Klaus David, Stephan Sigg, Rico Kusber, Brian D. Ziebart, Sian Lun Lau:
3rd workshop on recent advances in behavior prediction and pro-active pervasive computing. UbiComp Adjunct 2014: 415-420 - [c23]Anqi Liu, Brian D. Ziebart:
Robust Classification Under Sample Selection Bias. NIPS 2014: 37-45 - 2013
- [j1]Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
The Principle of Maximum Causal Entropy for Estimating Interacting Processes. IEEE Trans. Inf. Theory 59(4): 1966-1980 (2013) - [c22]Brian D. Ziebart:
Robust structure estimation of maximum causal entropy processes. Allerton 2013: 996-1001 - [c21]Christian Koehler, Brian D. Ziebart, Jennifer Mankoff, Anind K. Dey:
TherML: occupancy prediction for thermostat control. UbiComp 2013: 103-112 - [c20]Klaus David, Bernd Niklas Klein, Sian Lun Lau, Stephan Sigg, Brian D. Ziebart:
2nd workshop on recent advances in behavior prediction and pro-active pervasive computing. UbiComp (Adjunct Publication) 2013: 435-440 - [i3]Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. CoRR abs/1308.3506 (2013) - 2012
- [c19]Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert:
Activity Forecasting. ECCV (4) 2012: 201-214 - [c18]Brian D. Ziebart, Miroslav Dudík, Geoffrey J. Gordon, Katia P. Sycara, Wendi L. Adair, Jeanne M. Brett:
Identifying Culture and Leveraging Cultural Differences for Negotiation Agents. HICSS 2012: 618-627 - [c17]Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell:
Probabilistic pointing target prediction via inverse optimal control. IUI 2012: 1-10 - [i2]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) - 2011
- [c16]Brian D. Ziebart:
Factorized decision forecasting via combining value-based and reward-based estimation. Allerton 2011: 966-973 - [c15]Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
Maximum causal entropy correlated equilibria for Markov games. AAMAS 2011: 207-214 - [c14]Scott Davidoff, Brian D. Ziebart, John Zimmerman, Anind K. Dey:
Learning patterns of pick-ups and drop-offs to support busy family coordination. CHI 2011: 1175-1184 - [c13]Kevin Waugh, Brian D. Ziebart, Drew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. ICML 2011: 1169-1176 - [i1]Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell:
Computational Rationalization: The Inverse Equilibrium Problem. CoRR abs/1103.5254 (2011) - 2010
- [b1]Brian D. Ziebart:
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy. Carnegie Mellon University, USA, 2010 - [c12]Brian D. Ziebart, Drew Bagnell, Anind K. Dey:
Maximum Causal Entropy Correlated Equilibria for Markov Games. Interactive Decision Theory and Game Theory 2010 - [c11]Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey:
Modeling Interaction via the Principle of Maximum Causal Entropy. ICML 2010: 1255-1262
2000 – 2009
- 2009
- [c10]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- - [c9]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 - [c8]Nathan D. Ratliff, Brian D. Ziebart, Kevin M. Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa:
Inverse Optimal Heuristic Control for Imitation Learning. AISTATS 2009: 424-431 - 2008
- [c7]Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey:
Maximum Entropy Inverse Reinforcement Learning. AAAI 2008: 1433-1438 - [c6]Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell:
Fast Planning for Dynamic Preferences. ICAPS 2008: 412-419 - [c5]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 - 2007
- [c4]Brian D. Ziebart, Anind K. Dey, James A. Bagnell:
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. UAI 2007: 458-465 - 2005
- [c3]Brian D. Ziebart, Dan Roth, Roy H. Campbell, Anind K. Dey:
Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management. ICAC 2005: 204-215 - [c2]Anand Ranganathan, Jalal Al-Muhtadi, Jacob T. Biehl, Brian D. Ziebart, Roy H. Campbell, Brian P. Bailey:
Towards a Pervasive Computing Benchmark. PerCom Workshops 2005: 194-198 - 2003
- [c1]Manuel Román, Brian D. Ziebart, Roy H. Campbell:
Dynamic Application Composition: Customizing the Behavior of an Active Space. PerCom 2003: 169-
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from , , and to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 22:11 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint