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.
We propose a robust adversarial prediction framework for general multiclass classification.
Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.
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.
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions.
Many predicted structured objects (e. g., sequences, matchings, trees) are evaluated using the F-score, alignment error rate (AER), or other multivariate performance measures.
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.
Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods.
Ranked #5 on Word Sense Induction on SemEval 2013