7 citations · 10 across the 4 of their papers we have counts for
4 papers
Kernel Robust Bias-Aware Prediction under Covariate Shift
Anqi Liu, Rizal Fathony, Brian D. Ziebart
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challeng…
Robust Covariate Shift Prediction with General Losses and Feature Views
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. Unfortunately,…
Adversarial Structured Prediction for Multivariate Measures
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. Since…
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
Sima Behpour, Kris M. Kitani, Brian D. Ziebart
The use of random perturbations of ground truth data, such as random translation or scaling of bounding boxes, is a common heuristic used for data augmentation that has been shown…