Brian D. Ziebart

发表

A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment

Jing Wang, Clement T. Yu, Kevin Gimpel, 2015, TACL.

A Minimax Robust Approach for Learning to Assist Users with Pointing Tasks

Brian D. Ziebart, Sima Behpour, 2015, AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments.

Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management

Dan Roth, Roy H. Campbell, Anind K. Dey, 2005, Second International Conference on Autonomic Computing (ICAC'05).

Maximum Causal Entropy Correlated Equilibria for Markov Games

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2011, Interactive Decision Theory and Game Theory.

Maximum Entropy Inverse Reinforcement Learning

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2008, AAAI.

Distributionally Robust Graphical Models

Xinhua Zhang, Brian D. Ziebart, Rizal Fathony, 2018, NeurIPS.

Activity Forecasting

Martial Hebert, J. Andrew Bagnell, Kris M. Kitani, 2012, ECCV.

Robust structure estimation of maximum causal entropy processes

Brian D. Ziebart, 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Adversarial Learning for 3D Matching

Wei Xing, Brian D. Ziebart, Wei W. Xing, 2020, UAI.

Dynamic application composition

Roy H. Campbell, Brian D. Ziebart, Manuel Roman, 2003 .

The Principle of Maximum Causal Entropy for Estimating Interacting Processes

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2013, IEEE Transactions on Information Theory.

Inverse Optimal Heuristic Control for Imitation Learning

Siddhartha S. Srinivasa, Martial Hebert, Anind K. Dey, 2009, AISTATS.

Dynamic application composition: customizing the behavior of an active space

Roy H. Campbell, Manuel Román, Brian D. Ziebart, 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

Modeling Interaction via the Principle of Maximum Causal Entropy

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2010, ICML.

Leveraging Machine Learning to Improve Unwanted Resource Filtering

Chris Kanich, Brian D. Ziebart, Minaxi Gupta, 2014, AISec '14.

Planning-based prediction for pedestrians

Siddhartha S. Srinivasa, Martial Hebert, Anind K. Dey, 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Human Behavior Modeling with Maximum Entropy Inverse Optimal Control

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2009, AAAI Spring Symposium: Human Behavior Modeling.

Adversarial Sequence Tagging

Hong Wang, Tanya Y. Berger-Wolf, Jia Li, 2016, IJCAI.

2nd workshop on recent advances in behavior prediction and pro-active pervasive computing

Sian Lun Lau, Stephan Sigg, Klaus David, 2013, UbiComp.

A Game-Theoretic Adversarial Approach to Dynamic Network Prediction

Tanya Y. Berger-Wolf, Jia Li, Brian D. Ziebart, 2018, PAKDD.

Probabilistic pointing target prediction via inverse optimal control

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2012, IUI '12.

Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2008, UbiComp.

Identifying Culture and Leveraging Cultural Differences for Negotiation Agents

Miroslav Dudík, Katia P. Sycara, Geoffrey J. Gordon, 2012, 2012 45th Hawaii International Conference on System Sciences.

Adversarial Prediction Games for Multivariate Losses

Hong Wang, Brian D. Ziebart, Wei Xing, 2015, NIPS.

Adversarial Structured Prediction for Multivariate Measures pdf

Hong Wang, Brian D. Ziebart, Ashkan Rezaei, 2017, ArXiv.

Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs

Didem Ozevin, Lu Zhang, Brian D. Ziebart, 2020, J. Intell. Manuf..

Kernel Robust Bias-Aware Prediction under Covariate Shift pdf

Brian D. Ziebart, Anqi Liu, Rizal Fathony, 2017, ArXiv.

Adversarial Cost-Sensitive Classification

Brian D. Ziebart, Wei Xing, Sima Behpour, 2015, UAI.

TherML: occupancy prediction for thermostat control

Anind K. Dey, Jennifer Mankoff, Brian D. Ziebart, 2013, UbiComp.

Adversarial Multiclass Classification: A Risk Minimization Perspective

Brian D. Ziebart, Anqi Liu, Rizal Fathony, 2016, NIPS.

Learning patterns of pick-ups and drop-offs to support busy family coordination

John Zimmerman, Anind K. Dey, Scott Davidoff, 2011, CHI.

Context Retrieval for Web Tables

Jing Wang, Hong Wang, Clement T. Yu, 2015, ICTIR.

Strategy Transfer Learning via Parametric Deviation Sets

Kevin Waugh, Geoffrey J. Gordon, Katia Sycara, 2011 .

Computational Rationalization: The Inverse Equilibrium Problem pdf

Kevin Waugh, J. Andrew Bagnell, Brian D. Ziebart, 2011, ICML.

Robust Covariate Shift Prediction with General Losses and Feature Views pdf

Brian D. Ziebart, Anqi Liu, Anqi Liu, 2017, ArXiv.

Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis

Bing Liu, Ben S. Gerber, Barbara Di Eugenio, 2020, SIGDIAL.

Robust Covariate Shift Regression

Brian D. Ziebart, Anqi Liu, Mathew Monfort, 2016, AISTATS.

Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation

Brian D. Ziebart, Anqi Liu, Mathew Monfort, 2015, AAAI.

Social information improves location prediction in the wild

Tanya Y. Berger-Wolf, Damien R. Farine, Jia Li, 2015, AAAI 2015.

Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer

Brian D. Ziebart, Peter Carr, Mathew Monfort, 2016, UAI.

Fair Logistic Regression: An Adversarial Perspective pdf

Brian D. Ziebart, Rizal Fathony, Ashkan Rezaei, 2019, ArXiv.

ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection

Brian D. Ziebart, Sima Behpour, Kris M. Kitani, 2017 .

Serendipity-empowered path planning for predictive task completion

Anind K. Dey, Choonsung Shin, Brian D. Ziebart, 2015, J. Ambient Intell. Smart Environ..

Towards a pervasive computing benchmark

Brian P. Bailey, Roy H. Campbell, Anand Ranganathan, 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

Adversarially Optimizing Intersection over Union for Object Localization Tasks pdf

Kris M. Kitani, Brian D. Ziebart, Sima Behpour, 2017, ArXiv.

Adversarial Surrogate Losses for Ordinal Regression

Brian D. Ziebart, Rizal Fathony, Mohammad Ali Bashiri, 2017, NIPS.

Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction

Brian D. Ziebart, Anqi Liu, Lev Reyzin, 2015, AAAI.

Consistent Robust Adversarial Prediction for General Multiclass Classification pdf

Xinhua Zhang, Brian D. Ziebart, Anqi Liu, 2018, ArXiv.

Softstar: Heuristic-Guided Probabilistic Inference

Joshua B. Tenenbaum, Patrick Lucey, Brenden M. Lake, 2015, NIPS.

ADA: Adversarial Data Augmentation for Object Detection

Kris M. Kitani, Brian D. Ziebart, Sima Behpour, 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

Deep Correspondence Learning for Effective Robotic Teleoperation using Virtual Reality

George Maratos, Brian D. Ziebart, Tejas Sarma, 2019, 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids).

Factorized decision forecasting via combining value-based and reward-based estimation

Brian D. Ziebart, 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Graph-Based Inverse Optimal Control for Robot Manipulation

Byron Boots, Dieter Fox, Brian D. Ziebart, 2015, IJCAI.

Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2007, UAI.

Recurrent Neural Networks for Hierarchically Mapping Human-Robot Poses

Brian D. Ziebart, Zainab Al-Qurashi, 2020, 2020 Fourth IEEE International Conference on Robotic Computing (IRC).

Discriminatively Learning Inverse Optimal Control Models for Predicting Human Intentions

Brian D. Ziebart, Sanket Gaurav, 2019, AAMAS.

Hybrid Algorithm for Inverse Kinematics Using Deep Learning and Coordinate Transformation

Brian D. Ziebart, Zainab Al-Qurashi, 2019, 2019 Third IEEE International Conference on Robotic Computing (IRC).

Fast Planning for Dynamic Preferences

Anind K. Dey, J. Andrew Bagnell, Brian D. Ziebart, 2008, ICAPS.

Active Learning for Probabilistic Structured Prediction of Cuts and Matchings

Brian D. Ziebart, Anqi Liu, Sima Behpour, 2019, ICML.

Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems

Brian D. Ziebart, Xiangli Chen, Xiangli Chen, 2015, AISTATS.

Robust Classification Under Sample Selection Bias

Brian D. Ziebart, Anqi Liu, Anqi Liu, 2014, NIPS.

ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-label Classification

Wei Xing, Brian D. Ziebart, Sima Behpour, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Towards Building a Virtual Assistant Health Coach

Ben S. Gerber, Barbara Di Eugenio, Bing Liu, 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).

Efficient and Consistent Adversarial Bipartite Matching

Xinhua Zhang, Brian D. Ziebart, Sima Behpour, 2018, ICML.

Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes

Marek Petrik, Brian D. Ziebart, Andrea Tirinzoni, 2018 .

Modeling Health Coaching Dialogues for Behavioral Goal Extraction

Bing Liu, Ben S. Gerber, Barbara Di Eugenio, 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy

J. Andrew Bagnell, Brian D. Ziebart, J. Bagnell, 2010 .

Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches

Ben S. Gerber, Barbara Di Eugenio, Brian D. Ziebart, 2021, SIGDIAL.

Goal-predictive robotic teleoperation from noisy sensors

Brian D. Ziebart, Mathew Monfort, C. Schultz, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

3 Adversarial Sequence Tagging Games

Brian D. Ziebart, T. Berger-Wolf, Jia Li, 2016 .

Layered Hybrid Inverse Optimal Control for Learning Robot Manipulation from Demonstration

Byron Boots, Dieter Fox, Brian D. Ziebart, 2014 .

Inferring and Learning Subgoal Sequences

Brian D. Ziebart, Kaiser Asif, 2013 .

Modeling Decision Making with Maximum Entropy Inverse Optimal Control Thesis Proposal

Brian D. Ziebart, 2008 .

Process-conditioned investing with incomplete information using maximum causal entropy

Brian D. Ziebart, 2012 .

Robust Covariate Shift Classification Using Multiple Feature Views

Brian D. Ziebart, Anqi Liu, Hong Wang, 2016 .

Both Nearest Neighbours and Long-term Affiliates Predict Individual Locations During Collective Movement in Wild Baboons

Damien R. Farine, Brian D. Ziebart, Tanya Berger-Wolf, 2016, Scientific Reports.

Adversarial Methods Improve Object Localization

Brian D. Ziebart, Sima Behpour, Sima Behpour, 2016 .

Imitation Learning Across Embodiments via Adversarial Structured Prediction

Brian D. Ziebart, Xiangli Chen, 2015 .

Towards Enhancing Health Coaching Dialogue in Low-Resource Settings

Brian D. Ziebart, L. Sharp, Barbara Maria Di Eugenio, 2024, COLING.

Towards Uniformly Superhuman Autonomy via Subdominance Minimization

Brian D. Ziebart, Sanjiban Choudhury, Paul Vernaza, 2022, ICML.

Robust Fairness under Covariate Shift

Brian D. Ziebart, Anqi Liu, Omid Memarrast, 2020, AAAI.

A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields

Brian D. Ziebart, G. E. Marai, Mathew Monfort, 2017 .

Feedback in Imitation Learning: The Three Regimes of Covariate Shift

Brian D. Ziebart, J. Bagnell, Arun Venkatraman, 2021, ArXiv.

Distributionally Robust Imitation Learning

Brian D. Ziebart, Mohammad Ali Bashiri, Xinhua Zhang, 2021, NeurIPS.

Risk-averse policy optimization via risk-neutral policy optimization

Brian D. Ziebart, Marcello Restelli, Andrea Tirinzoni, 2022, Artif. Intell..

Fairness for Robust Log Loss Classification

Brian D. Ziebart, Rizal Fathony, Ashkan Rezaei, 2019, AAAI.

Activity Forecasting

Kris M. Kitani, Brian D. Ziebart, M. Hebert, 2012, ECCV.

Superhuman Fairness

Brian D. Ziebart, Omid Memarrast, Linh Vu, 2023, ArXiv.

Fairness for Robust Learning to Rank pdf

Brian D. Ziebart, Rizal Fathony, Ashkan Rezaei, 2021, PAKDD.

Maximum Entropy Inverse Reinforcement Learning

Brian D. Ziebart, Andrew L. Maas, J. Bagnell, 2008, AAAI.

Moment Distributionally Robust Tree Structured Prediction

Brian D. Ziebart, Kevin Gimpel, Xinhua Zhang, 2022, NeurIPS.

Faculty List

Michael T. Kirschenheiter, Jon A. Solworth, Ian A. Kash, 2018, International journal of urology.

A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study

Brian D. Ziebart, L. Sharp, Barbara Di Eugenio, 2021, JMIR formative research.

Maximum Entropy Inverse Reinforcement Learning

Brian D. Ziebart, J. Bagnell, A. Dey, 2008, AAAI.

Distributionally Robust Skeleton Learning of Discrete Bayesian Networks

Brian D. Ziebart, Yeshu Li, 2023, ArXiv.

Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation

Brian D. Ziebart, Barbara Maria Di Eugenio, Bing Liu, 2024, 2404.10268.

Goal Summarization for Human-Human Health Coaching Dialogues

Bing Liu, Barbara Di Eugenio, Brian D. Ziebart, 2020, FLAIRS.

Creating and Annotating a Corpus of Health Coaching Dialogue

Brian D. Ziebart, L. Sharp, Barbara Maria Di Eugenio, 2018 .

Towards a Virtual Assistant Health Coach : Corpus Collection and Annotations

Brian D. Ziebart, Barbara Maria Di Eugenio, Itika Gupta, 2018 .

Feedback in Imitation Learning: The Three Regimes of Covariate Shift pdf

J. Andrew Bagnell, Sanjiban Choudhury, Brian D. Ziebart, 2021, ArXiv.