389 citations · 2.3k across the 27 of their papers we have counts for
8 papers · 1 filter
Optimizing ML Training with Metagradient Descent
Logan Engstrom, Andrew Ilyas, Benjamin Chen +3
A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a va…
Datamodels: Predicting Predictions from Training Data
Andrew Ilyas, Sung Min Park, Logan Engstrom +2
We present a conceptual framework, datamodeling, for analyzing the behavior of a model class in terms of the training data. For any fixed "target" example , training set , an…
Identifying Statistical Bias in Dataset Replication
Logan Engstrom, Andrew Ilyas, Shibani Santurkar +3
Dataset replication is a useful tool for assessing whether improvements in test accuracy on a specific benchmark correspond to improvements in models' ability to generalize reliabl…
Adversarial Robustness as a Prior for Learned Representations
Logan Engstrom, Andrew Ilyas, Shibani Santurkar +3
An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings…
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras +3
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial…
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
Logan Engstrom, Andrew Ilyas, Anish Athalye
We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing ach…