no code implementations • SIGDIAL (ACL) 2021 • Itika Gupta, Barbara Di Eugenio, Brian D. Ziebart, Bing Liu, Ben S. Gerber, Lisa K. Sharp
In this paper, we present our work towards assisting health coaches by extracting the physical activity goal the user and coach negotiate via text messages.
2 code implementations • 18 Dec 2018 • Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Ali Bashiri, Wei Xing, Sima Behpour, Xinhua Zhang, Brian D. Ziebart
We propose a robust adversarial prediction framework for general multiclass classification.
no code implementations • NeurIPS 2018 • Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart
Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.
no code implementations • 28 Dec 2017 • Anqi Liu, Rizal Fathony, Brian D. Ziebart
Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution.
no code implementations • 28 Dec 2017 • Anqi Liu, Brian D. Ziebart
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions.
no code implementations • 20 Dec 2017 • Hong Wang, Ashkan Rezaei, Brian D. Ziebart
Many predicted structured objects (e. g., sequences, matchings, trees) are evaluated using the F-score, alignment error rate (AER), or other multivariate performance measures.
no code implementations • 21 Oct 2017 • Sima Behpour, Kris M. Kitani, Brian D. Ziebart
We aim to find an optimal adversarial perturbations of the ground truth data (i. e., the worst case perturbations) that forces the object bounding box predictor to learn from the hardest distribution of perturbed examples for better test-time performance.
no code implementations • TACL 2015 • Jing Wang, Mohit Bansal, Kevin Gimpel, Brian D. Ziebart, Clement T. Yu
Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods.
no code implementations • 15 Aug 2013 • Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task.