Search Results for author: Brian D. Ziebart

Found 9 papers, 1 papers with code

Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches

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.

Distributionally Robust Graphical Models

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.

Structured Prediction

Kernel Robust Bias-Aware Prediction under Covariate Shift

no code implementations28 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.

Robust Covariate Shift Prediction with General Losses and Feature Views

no code implementations28 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.

Adversarial Structured Prediction for Multivariate Measures

no code implementations20 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.

named-entity-recognition Named Entity Recognition +3

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

no code implementations21 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.

Data Augmentation Object +3

Computational Rationalization: The Inverse Equilibrium Problem

no code implementations15 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.

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