37 citations · 64 across the 16 of their papers we have counts for
5 papers · 1 filter
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Mazda Moayeri, Wenxiao Wang, Sahil Singla +1
We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model traini…
Data-Centric Debugging: mitigating model failures via targeted data collection
Sahil Singla, Atoosa Malemir Chegini, Mazda Moayeri +1
Deep neural networks can be unreliable in the real world when the training set does not adequately cover all the settings where they are deployed. Focusing on image classification,…
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Mazda Moayeri, Kiarash Banihashem, Soheil Feizi
Several existing works study either adversarial or natural distributional robustness of deep neural networks separately. In practice, however, models need to enjoy both types of ro…
Core Risk Minimization using Salient ImageNet
Sahil Singla, Mazda Moayeri, Soheil Feizi
Deep neural networks can be unreliable in the real world especially when they heavily use spurious features for their predictions. Recently, Singla & Feizi (2022) introduced the Sa…
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes
Mazda Moayeri, Phillip Pope, Yogesh Balaji +1
While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how model…