Brian D. Ziebart

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 51

Top Topics

Structured Prediction

Ordinal Regression

Markov Networks

Covariate Shift

Top Venues

Publications

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 (2024)
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 (2024)
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)
Yeshu Li, Brian D. Ziebart
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks. NeurIPS (2023)
Omid Memarrast, Linh Vu, Brian D. Ziebart
Superhuman Fairness. ICML (2023)
Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart
Fairness for Robust Learning to Rank. PAKDD (1) (2023)
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)
Yeshu Li, Brian D. Ziebart
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks. CoRR (2023)
Omid Memarrast, Linh Vu, Brian D. Ziebart
Superhuman Fairness. CoRR (2023)
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)
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 (2022)
Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian D. Ziebart, Kevin Gimpel
Moment Distributionally Robust Tree Structured Prediction. NeurIPS (2022)
Yeshu Li, Zhan Shi, Xinhua Zhang, Brian D. Ziebart
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks. AISTATS (2022)
Brian D. Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza
Towards Uniformly Superhuman Autonomy via Subdominance Minimization. ICML (2022)
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) (2022)
Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian D. Ziebart
Fairness for Robust Learning to Rank. CoRR (2021)
Jonathan C. Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian D. Ziebart, J. Andrew Bagnell
Feedback in Imitation Learning: The Three Regimes of Covariate Shift. CoRR (2021)
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)
Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart
Robust Fairness Under Covariate Shift. AAAI (2021)
Mohammad Ali Bashiri, Brian D. Ziebart, Xinhua Zhang
Distributionally Robust Imitation Learning. NeurIPS (2021)
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)
Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart
Fairness for Robust Log Loss Classification. AAAI (2020)
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 Conference (2020)
Zainab Al-Qurashi, Brian D. Ziebart
Recurrent Neural Networks for Hierarchically Mapping Human-Robot Poses. IRC (2020)
Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart
Robust Fairness under Covariate Shift. CoRR (2020)
Wei Xing, Brian D. Ziebart
Adversarial Learning for 3D Matching. UAI (2020)
Zainab Al-Qurashi, Brian D. Ziebart
Hybrid Algorithm for Inverse Kinematics Using Deep Learning and Coordinate Transformation. IRC (2019)
Sanket Gaurav, Brian D. Ziebart
Discriminatively Learning Inverse Optimal Control Models for Predicting Human Intentions. AAMAS (2019)
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)
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)
Sima Behpour, Kris M. Kitani, Brian D. Ziebart
ADA: Adversarial Data Augmentation for Object Detection. WACV (2019)
Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart
Fair Logistic Regression: An Adversarial Perspective. CoRR (2019)
Sima Behpour, Anqi Liu, Brian D. Ziebart
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. ICML (2019)
Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
Efficient and Consistent Adversarial Bipartite Matching. ICML (2018)
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 (2018)
Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian D. Ziebart
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. NeurIPS (2018)
Jia Li, Brian D. Ziebart, Tanya Y. Berger-Wolf
A Game-Theoretic Adversarial Approach to Dynamic Network Prediction. PAKDD (3) (2018)
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)
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
Distributionally Robust Graphical Models. CoRR (2018)
Sima Behpour, Wei Xing, Brian D. Ziebart
ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification. AAAI (2018)
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
Distributionally Robust Graphical Models. NeurIPS (2018)
Christopher Schultz, Sanket Gaurav, Mathew Monfort, Lingfei Zhang, Brian D. Ziebart
Goal-predictive robotic teleoperation from noisy sensors. ICRA (2017)
Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart
Adversarial Surrogate Losses for Ordinal Regression. NIPS (2017)
Sima Behpour, Kris M. Kitani, Brian D. Ziebart
Adversarially Optimizing Intersection over Union for Object Localization Tasks. CoRR (2017)
Anqi Liu, Brian D. Ziebart
Robust Covariate Shift Prediction with General Losses and Feature Views. CoRR (2017)
Hong Wang, Ashkan Rezaei, Brian D. Ziebart
Adversarial Structured Prediction for Multivariate Measures. CoRR (2017)
Anqi Liu, Rizal Fathony, Brian D. Ziebart
Kernel Robust Bias-Aware Prediction under Covariate Shift. CoRR (2017)
Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Y. Berger-Wolf
Adversarial Sequence Tagging. IJCAI (2016)
Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart
Robust Covariate Shift Regression. AISTATS (2016)
Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart
Adversarial Multiclass Classification: A Risk Minimization Perspective. NIPS (2016)
Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr
Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer. UAI (2016)
Mathew Monfort, Anqi Liu, Brian D. Ziebart
Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. AAAI (2015)
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 (2015)
Anqi Liu, Lev Reyzin, Brian D. Ziebart
Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction. AAAI (2015)
Xiangli Chen, Brian D. Ziebart
Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems. AISTATS (2015)
Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum
Softstar: Heuristic-Guided Probabilistic Inference. NIPS (2015)
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)
Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox
Graph-Based Inverse Optimal Control for Robot Manipulation. IJCAI (2015)
Hong Wang, Anqi Liu, Jing Wang, Brian D. Ziebart, Clement T. Yu, Warren Shen
Context Retrieval for Web Tables. ICTIR (2015)
Hong Wang, Wei Xing, Kaiser Asif, Brian D. Ziebart
Adversarial Prediction Games for Multivariate Losses. NIPS (2015)
Choonsung Shin, Brian D. Ziebart, Anind K. Dey
Serendipity-empowered path planning for predictive task completion. J. Ambient Intell. Smart Environ. 7 (5) (2015)
Kaiser Asif, Wei Xing, Sima Behpour, Brian D. Ziebart
Adversarial Cost-Sensitive Classification. UAI (2015)
Anqi Liu, Brian D. Ziebart
Robust Classification Under Sample Selection Bias. NIPS (2014)
Klaus David, Stephan Sigg, Rico Kusber, Brian D. Ziebart, Sian Lun Lau
workshop on recent advances in behavior prediction and pro-active pervasive computing. UbiComp Adjunct (2014)
Sruti Bhagavatula, Christopher W. Dunn, Chris Kanich, Minaxi Gupta, Brian D. Ziebart
Leveraging Machine Learning to Improve Unwanted Resource Filtering. AISec@CCS (2014)
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) (2013)
Christian Koehler, Brian D. Ziebart, Jennifer Mankoff, Anind K. Dey
TherML: occupancy prediction for thermostat control. UbiComp (2013)
Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
Computational Rationalization: The Inverse Equilibrium Problem. CoRR (2013)
Brian D. Ziebart
Robust structure estimation of maximum causal entropy processes. Allerton (2013)
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)
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)
Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification CoRR (2012)
Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert
Activity Forecasting. ECCV (4) (2012)
Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
Probabilistic pointing target prediction via inverse optimal control. IUI (2012)
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)
Kevin Waugh, Brian D. Ziebart, Drew Bagnell
Computational Rationalization: The Inverse Equilibrium Problem. ICML (2011)
Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
Maximum causal entropy correlated equilibria for Markov games. AAMAS (2011)
Brian D. Ziebart
Factorized decision forecasting via combining value-based and reward-based estimation. Allerton (2011)
Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
Computational Rationalization: The Inverse Equilibrium Problem CoRR (2011)
Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey
Modeling Interaction via the Principle of Maximum Causal Entropy. ICML (2010)
Brian D. Ziebart, Drew Bagnell, Anind K. Dey
Maximum Causal Entropy Correlated Equilibria for Markov Games. Interactive Decision Theory and Game Theory (2010)
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)
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)
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)
Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
Fast Planning for Dynamic Preferences. ICAPS (2008)
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)
Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey
Maximum Entropy Inverse Reinforcement Learning. AAAI (2008)
Brian D. Ziebart, Anind K. Dey, James A. Bagnell
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. UAI (2007)
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)
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)
Manuel Román, Brian D. Ziebart, Roy H. Campbell
Dynamic Application Composition: Customizing the Behavior of an Active Space. PerCom (2003)