Andrew Ilyas

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 58

Top Topics

Policy Gradient

Top Venues

Publications

Harshay Shah, Andrew Ilyas, Aleksander Madry
Decomposing and Editing Predictions by Modeling Model Computation. CoRR (2024)
Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry
Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection. CoRR (2024)
Sarah H. Cen, Andrew Ilyas, Jennifer Allen, Hannah Li, Aleksander Madry
Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content. CoRR (2024)
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry
TRAK: Attributing Model Behavior at Scale. CoRR (2023)
Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry
Rethinking Backdoor Attacks. CoRR (2023)
Sarah H. Cen, Andrew Ilyas, Aleksander Madry
User Strategization and Trustworthy Algorithms. CoRR (2023)
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry
Raising the Cost of Malicious AI-Powered Image Editing. ICML (2023)
Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry
Rethinking Backdoor Attacks. ICML (2023)
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry
FFCV: Accelerating Training by Removing Data Bottlenecks. CVPR (2023)
Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry
ModelDiff: A Framework for Comparing Learning Algorithms. ICML (2023)
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry
Raising the Cost of Malicious AI-Powered Image Editing. CoRR (2023)
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
What Makes a Good Fisherman? Linear Regression under Self-Selection Bias. STOC (2023)
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry
TRAK: Attributing Model Behavior at Scale. ICML (2023)
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry
FFCV: Accelerating Training by Removing Data Bottlenecks. CoRR (2023)
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry
Datamodels: Understanding Predictions with Data and Data with Predictions. ICML (2022)
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
Estimation of Standard Auction Models. EC (2022)
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
Estimation of Standard Auction Models. CoRR (2022)
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
3DB: A Framework for Debugging Computer Vision Models. NeurIPS (2022)
Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry
When does Bias Transfer in Transfer Learning? CoRR (2022)
Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry
ModelDiff: A Framework for Comparing Learning Algorithms. CoRR (2022)
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias. CoRR (2022)
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry
Datamodels: Predicting Predictions from Training Data. CoRR (2022)
Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor
Unadversarial Examples: Designing Objects for Robust Vision. NeurIPS (2021)
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
3DB: A Framework for Debugging Computer Vision Models. CoRR (2021)
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
Noise or Signal: The Role of Image Backgrounds in Object Recognition. ICLR (2021)
Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis
A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. AISTATS (2020)
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. CoRR (2020)
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry
Do Adversarially Robust ImageNet Models Transfer Better? CoRR (2020)
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry
Do Adversarially Robust ImageNet Models Transfer Better? NeurIPS (2020)
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
Noise or Signal: The Role of Image Backgrounds in Object Recognition. CoRR (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
Implementation Matters in Deep RL: A Case Study on PPO and TRPO. ICLR (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
Identifying Statistical Bias in Dataset Replication. ICML (2020)
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
A Closer Look at Deep Policy Gradients. ICLR (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO. CoRR (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
Identifying Statistical Bias in Dataset Replication. CoRR (2020)
Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor
Unadversarial Examples: Designing Objects for Robust Vision. CoRR (2020)
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. ICML (2020)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry
Learning Perceptually-Aligned Representations via Adversarial Robustness. CoRR (2019)
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Adversarial Examples Are Not Bugs, They Are Features. NeurIPS (2019)
Andrew Ilyas, Logan Engstrom, Aleksander Madry
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors. ICLR (Poster) (2019)
Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Image Synthesis with a Single (Robust) Classifier. NeurIPS (2019)
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Madry
Computer Vision with a Single (Robust) Classifier. CoRR (2019)
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Adversarial Examples Are Not Bugs, They Are Features. CoRR (2019)
Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez, Samuel Madden
Extracting Syntactical Patterns from Databases. ICDE (2018)
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
How Does Batch Normalization Help Optimization? NeurIPS (2018)
Logan Engstrom, Andrew Ilyas, Anish Athalye
Evaluating and Understanding the Robustness of Adversarial Logit Pairing. CoRR (2018)
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
Training GANs with Optimism. ICLR (Poster) (2018)
Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Black-box Adversarial Attacks with Limited Queries and Information. ICML (2018)
Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Black-box Adversarial Attacks with Limited Queries and Information. CoRR (2018)
Andrew Ilyas, Logan Engstrom, Aleksander Madry
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors. CoRR (2018)
Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok
Synthesizing Robust Adversarial Examples. ICML (2018)
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift). CoRR (2018)
Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms? CoRR (2018)
Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok
Synthesizing Robust Adversarial Examples. CoRR (2017)
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
Training GANs with Optimism. CoRR (2017)
Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Query-Efficient Black-box Adversarial Examples. CoRR (2017)
Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez, Samuel Madden
Extracting Syntactic Patterns from Databases. CoRR (2017)
Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, Alexandros G. Dimakis
The Robust Manifold Defense: Adversarial Training using Generative Models. CoRR (2017)
Andrew Ilyas
MicroFilters: Harnessing twitter for disaster management. GHTC (2014)