A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations. - Logan Engstrom; Dimitris Tsipras; Ludwig Schmidt; Aleksander Madry (CoRR, 2017)

Query-Efficient Black-box Adversarial Examples. - Andrew Ilyas; Logan Engstrom; Anish Athalye; Jessy Lin (CoRR, 2017)

Synthesizing Robust Adversarial Examples. - Anish Athalye; Logan Engstrom; Andrew Ilyas; Kevin Kwok (ICML, 2018)

Black-box Adversarial Attacks with Limited Queries and Information. - Andrew Ilyas; Logan Engstrom; Anish Athalye; Jessy Lin (ICML, 2018)

There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits). - Dimitris Tsipras; Shibani Santurkar; Logan Engstrom; Alexander Turner 001; Aleksander Madry (CoRR, 2018)

Evaluating and Understanding the Robustness of Adversarial Logit Pairing. - Logan Engstrom; Andrew Ilyas; Anish Athalye (CoRR, 2018)

Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms? - Andrew Ilyas; Logan Engstrom; Shibani Santurkar; Dimitris Tsipras; Firdaus Janoos; Larry Rudolph; Aleksander Madry (CoRR, 2018)

Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors. - Andrew Ilyas; Logan Engstrom; Aleksander Madry (ICLR (Poster), 2019)

Robustness May Be at Odds with Accuracy. - Dimitris Tsipras; Shibani Santurkar; Logan Engstrom; Alexander Turner 001; Aleksander Madry (ICLR (Poster), 2019)

Exploring the Landscape of Spatial Robustness. - Logan Engstrom; Brandon Tran; Dimitris Tsipras; Ludwig Schmidt; Aleksander Madry (ICML, 2019)

Adversarial Examples Are Not Bugs, They Are Features. - Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Logan Engstrom; Brandon Tran; Aleksander Madry (NeurIPS, 2019)

Image Synthesis with a Single (Robust) Classifier. - Shibani Santurkar; Andrew Ilyas; Dimitris Tsipras; Logan Engstrom; Brandon Tran; Aleksander Madry (NeurIPS, 2019)

Learning Perceptually-Aligned Representations via Adversarial Robustness. - Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Brandon Tran; Aleksander Madry (CoRR, 2019)

Computer Vision with a Single (Robust) Classifier. - Shibani Santurkar; Dimitris Tsipras; Brandon Tran; Andrew Ilyas; Logan Engstrom; Aleksander Madry (CoRR, 2019)

Implementation Matters in Deep RL: A Case Study on PPO and TRPO. - Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Firdaus Janoos; Larry Rudolph; Aleksander Madry (ICLR, 2020)

A Closer Look at Deep Policy Gradients. - Andrew Ilyas; Logan Engstrom; Shibani Santurkar; Dimitris Tsipras; Firdaus Janoos; Larry Rudolph; Aleksander Madry (ICLR, 2020)

Identifying Statistical Bias in Dataset Replication. - Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Jacob Steinhardt; Aleksander Madry (ICML, 2020)

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. - Dimitris Tsipras; Shibani Santurkar; Logan Engstrom; Andrew Ilyas; Aleksander Madry (ICML, 2020)

Do Adversarially Robust ImageNet Models Transfer Better? - Hadi Salman; Andrew Ilyas; Logan Engstrom; Ashish Kapoor; Aleksander Madry (NeurIPS, 2020)

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO. - Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Firdaus Janoos; Larry Rudolph; Aleksander Madry (CoRR, 2020)

Noise or Signal: The Role of Image Backgrounds in Object Recognition. - Kai Yuanqing Xiao; Logan Engstrom; Andrew Ilyas; Aleksander Madry (ICLR, 2021)

Unadversarial Examples: Designing Objects for Robust Vision. - Hadi Salman; Andrew Ilyas; Logan Engstrom; Sai Vemprala; Aleksander Madry; Ashish Kapoor (NeurIPS, 2021)

Datamodels: Understanding Predictions with Data and Data with Predictions. - Andrew Ilyas; Sung Min Park; Logan Engstrom; Guillaume Leclerc; Aleksander Madry (ICML, 2022)

3DB: A Framework for Debugging Computer Vision Models. - Guillaume Leclerc; Hadi Salman; Andrew Ilyas; Sai Vemprala; Logan Engstrom; Vibhav Vineet; Kai Yuanqing Xiao; Pengchuan Zhang; Shibani Santurkar; Greg Yang; Ashish Kapoor; Aleksander Madry (NeurIPS, 2022)

Datamodels: Predicting Predictions from Training Data. - Andrew Ilyas; Sung Min Park; Logan Engstrom; Guillaume Leclerc; Aleksander Madry (CoRR, 2022)

When does Bias Transfer in Transfer Learning? - Hadi Salman; Saachi Jain; Andrew Ilyas; Logan Engstrom; Eric Wong 001; Aleksander Madry (CoRR, 2022)

FFCV: Accelerating Training by Removing Data Bottlenecks. - Guillaume Leclerc; Andrew Ilyas; Logan Engstrom; Sung Min Park; Hadi Salman; Aleksander Madry (CVPR, 2023)

Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation. - Joshua Vendrow; Saachi Jain; Logan Engstrom; Aleksander Madry (CoRR, 2023)

DsDm: Model-Aware Dataset Selection with Datamodels. - Logan Engstrom; Axel Feldmann; Aleksander Madry (ICML, 2024)

Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale. - Alaa Khaddaj; Logan Engstrom; Aleksander Madry (ICLR, 2025)

Optimizing ML Training with Metagradient Descent. - Logan Engstrom; Andrew Ilyas; Benjamin Chen; Axel Feldmann; William Moses; Aleksander Madry (CoRR, 2025)

MAGIC: Near-Optimal Data Attribution for Deep Learning. - Andrew Ilyas; Logan Engstrom (CoRR, 2025)

DataMIL: Selecting Data for Robot Imitation Learning with Datamodels. - Shivin Dass; Alaa Khaddaj; Logan Engstrom; Aleksander Madry; Andrew Ilyas; Roberto Martín-Martín (CoRR, 2025)

Small-to-Large Generalization: Data Influences Models Consistently Across Scale. - Alaa Khaddaj; Logan Engstrom; Aleksander Madry (CoRR, 2025)