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Brian D. Ziebart
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- affiliation: University of Illinois at Chicago, Department of Computer Science
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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
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
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
Risk-averse policy optimization via risk-neutral policy optimization. Artif. Intell. 311: 103765 (2022) - [j4]Kaiser Asif, Lu Zhang
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
Modeling Health Coaching Dialogues for Behavioral Goal Extraction. BIBM 2019: 1188-1190 - [c50]Sanket Gaurav
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
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
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
TherML: occupancy prediction for thermostat control. UbiComp 2013: 103-112 - [c20]Klaus David, Bernd Niklas Klein, Sian Lun Lau
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
Activity Forecasting. ECCV (4) 2012: 201-214 - [c18]Brian D. Ziebart, Miroslav Dudík, Geoffrey J. Gordon, Katia P. Sycara, Wendi L. Adair
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
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
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
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last updated on 2024-10-07 21:18 CEST by the dblp team
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