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Found 9 papers, 1 papers with code
Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches
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.
Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multiclass classification.
Distributionally Robust Graphical Models
Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.
Kernel Robust Bias-Aware Prediction under Covariate Shift
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
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
Many predicted structured objects (e. g., sequences, matchings, trees) are evaluated using the F-score, alignment error rate (AER), or other multivariate performance measures.
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
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.
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods.
Computational Rationalization: The Inverse Equilibrium Problem
Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task.
