Andrew Ilyas

According to our database, Andrew Ilyas authored at least 42 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Attribute-to-Delete: Machine Unlearning via Datamodel Matching.

Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, Seth Neel

CoRR, 2024

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection.

Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry

CoRR, 2024

User Strategization and Trustworthy Algorithms.

Sarah H. Cen, Andrew Ilyas, Aleksander Madry

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content.

Sarah H. Cen, Andrew Ilyas, Jennifer Allen, Hannah Li, Aleksander Madry

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Decomposing and Editing Predictions by Modeling Model Computation.

Harshay Shah, Andrew Ilyas, Aleksander Madry

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

What Makes a Good Fisherman? Linear Regression under Self-Selection Bias.

Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

ModelDiff: A Framework for Comparing Learning Algorithms.

Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

Raising the Cost of Malicious AI-Powered Image Editing.

Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

TRAK: Attributing Model Behavior at Scale.

Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

Rethinking Backdoor Attacks.

Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

FFCV: Accelerating Training by Removing Data Bottlenecks.

Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

When does Bias Transfer in Transfer Learning?

Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry

CoRR, 2022

Datamodels: Predicting Predictions from Training Data.

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry

CoRR, 2022

Estimation of Standard Auction Models.

Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis

Proceedings of the EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Datamodels: Understanding Predictions with Data and Data with Predictions.

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2022

2021

Unadversarial Examples: Designing Objects for Robust Vision.

Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Noise or Signal: The Role of Image Backgrounds in Object Recognition.

Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Do Adversarially Robust ImageNet Models Transfer Better?

Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks.

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry

Proceedings of the 37th International Conference on Machine Learning, 2020

Identifying Statistical Bias in Dataset Replication.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry

Proceedings of the 37th International Conference on Machine Learning, 2020

A Closer Look at Deep Policy Gradients.

Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

Proceedings of the 8th International Conference on Learning Representations, 2020

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

Proceedings of the 8th International Conference on Learning Representations, 2020

A Theoretical and Practical Framework for Regression and Classification from Truncated Samples.

Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Computer Vision with a Single (Robust) Classifier.

Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Madry

CoRR, 2019

Learning Perceptually-Aligned Representations via Adversarial Robustness.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry

CoRR, 2019

Image Synthesis with a Single (Robust) Classifier.

Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Examples Are Not Bugs, They Are Features.

Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors.

Andrew Ilyas, Logan Engstrom, Aleksander Madry

Proceedings of the 7th International Conference on Learning Representations, 2019

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

Evaluating and Understanding the Robustness of Adversarial Logit Pairing.

Logan Engstrom, Andrew Ilyas, Anish Athalye

CoRR, 2018

How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift).

Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry

CoRR, 2018

How Does Batch Normalization Help Optimization?

Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Black-box Adversarial Attacks with Limited Queries and Information.

Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin

Proceedings of the 35th International Conference on Machine Learning, 2018

Synthesizing Robust Adversarial Examples.

Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok

Proceedings of the 35th International Conference on Machine Learning, 2018

Training GANs with Optimism.

Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng

Proceedings of the 6th International Conference on Learning Representations, 2018

Extracting Syntactical Patterns from Databases.

Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez, Samuel Madden

Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

2017

The Robust Manifold Defense: Adversarial Training using Generative Models.

Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, Alexandros G. Dimakis

CoRR, 2017

Query-Efficient Black-box Adversarial Examples.

Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin

CoRR, 2017

Extracting Syntactic Patterns from Databases.

Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez, Samuel Madden

CoRR, 2017

2014

MicroFilters: Harnessing twitter for disaster management.

Andrew Ilyas

Proceedings of the IEEE Global Humanitarian Technology Conference, 2014