Ziebart, Brian D

22 publications

NeurIPS 2025 Imitation Beyond Expectation Using Pluralistic Stochastic Dominance Ali Farajzadeh, Danyal Saeed, Syed M Abbas, Rushit N. Shah, Aadirupa Saha, Brian D Ziebart

IJCAI 2025 Imitation Learning via Focused Satisficing Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski, Ali Farajzadeh, Sanjiban Choudhury, Brian D. Ziebart

NeurIPSW 2024 Value-Aligned Imitation via Focused Satisficing Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski, Ali Farajzadeh, Sanjiban Choudhury, Brian D Ziebart

ICML 2023 Superhuman Fairness Omid Memarrast, Linh Vu, Brian D Ziebart

ICLRW 2023 Superhuman Fairness Omid Memarrast, Linh Vu, Brian D Ziebart

AAAI 2021 Robust Fairness Under Covariate Shift Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart

AAAI 2020 Fairness for Robust Log Loss Classification Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian D. Ziebart

WACV 2019 ADA: Adversarial Data Augmentation for Object Detection Sima Behpour, Kris M. Kitani, Brian D. Ziebart

AAAI 2018 ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification Sima Behpour, Wei Xing, Brian D. Ziebart

UAI 2016 Adversarial Inverse Optimal Control for General Imitation Learning Losses and Embodiment Transfer Xiangli Chen, Mathew Monfort, Brian D. Ziebart, Peter Carr

IJCAI 2016 Adversarial Sequence Tagging Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Y. Berger-Wolf

AISTATS 2016 Robust Covariate Shift Regression Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart

UAI 2015 Adversarial Cost-Sensitive Classification Kaiser Asif, Wei Xing, Sima Behpour, Brian D. Ziebart

IJCAI 2015 Graph-Based Inverse Optimal Control for Robot Manipulation Arunkumar Byravan, Mathew Monfort, Brian D. Ziebart, Byron Boots, Dieter Fox

AAAI 2015 Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation Mathew Monfort, Anqi Liu, Brian D. Ziebart

AISTATS 2015 Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems Xiangli Chen, Brian D. Ziebart

AAAI 2015 Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction Anqi Liu, Lev Reyzin, Brian D. Ziebart

ECCV 2012 Activity Forecasting Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, Martial Hebert

ICML 2011 Computational Rationalization: The Inverse Equilibrium Problem Kevin Waugh, Brian D. Ziebart, Drew Bagnell

ICML 2010 Modeling Interaction via the Principle of Maximum Causal Entropy Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey

AAAI 2008 Maximum Entropy Inverse Reinforcement Learning Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, Anind K. Dey

UAI 2007 Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification Brian D. Ziebart, Anind K. Dey, James A. Bagnell