170 citations · 229 across the 5 of their papers we have counts for
5 papers · 1 filter
Data Feedback Loops: Model-driven Amplification of Dataset Biases
Rohan Taori, Tatsunori B. Hashimoto
Datasets scraped from the internet have been critical to the successes of large-scale machine learning. Yet, this very success puts the utility of future internet-derived datasets…
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John Miller, Rohan Taori, Aditi Raghunathan +6
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distri…
Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar +3
We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbat…
Autoregressive Models: What Are They Good For?
Murtaza Dalal, Alexander C. Li, Rohan Taori
Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates. We investigate the use of AR models as density…
Targeted Adversarial Examples for Black Box Audio Systems
Rohan Taori, Amog Kamsetty, Brenton Chu +1
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adver…