Andrew Ilyas - RankMe

72 papers A* 27A 1Journal 41Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services. CoRR Aspen K. Hopkins, Sarah H. Cen, Andrew Ilyas, Isabella Struckman, Luis Videgaray, Aleksander Madry
2025	J	`jnl`	DataMIL: Selecting Data for Robot Imitation Learning with Datamodels. CoRR Shivin Dass, Alaa Khaddaj, Logan Engstrom, Aleksander Madry, Andrew Ilyas, Roberto Martín-Martín
2025	J	`jnl`	Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season. CoRR Sarah H. Cen, Andrew Ilyas, Hedi Driss, Charlotte Park, Aspen K. Hopkins, Chara Podimata, Aleksander Madry
2025	J	`jnl`	MAGIC: Near-Optimal Data Attribution for Deep Learning. CoRR Andrew Ilyas, Logan Engstrom
2025	A*	`conf`	Machine Unlearning via Simulated Oracle Matching. ICLR Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, Seth Neel
2025	J	`jnl`	Optimizing Canaries for Privacy Auditing with Metagradient Descent. CoRR Matteo Boglioni, Terrance Liu, Andrew Ilyas, Zhiwei Steven Wu
2025	J	`jnl`	Optimizing ML Training with Metagradient Descent. CoRR Logan Engstrom, Andrew Ilyas, Benjamin Chen, Axel Feldmann, William Moses, Aleksander Madry
2025	J	`jnl`	Probably Approximately Correct Labels. CoRR Emmanuel J. Candès, Andrew Ilyas, Tijana Zrnic
2024	J	`jnl`	Attribute-to-Delete: Machine Unlearning via Datamodel Matching. CoRR Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, Seth Neel
2024	J	`jnl`	Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection. CoRR Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry
2024	A*	`conf`	Decomposing and Editing Predictions by Modeling Model Computation. ICML Harshay Shah, Andrew Ilyas, Aleksander Madry
2024	J	`jnl`	Decomposing and Editing Predictions by Modeling Model Computation. CoRR Harshay Shah, Andrew Ilyas, Aleksander Madry
2024	A*	`conf`	Improving Subgroup Robustness via Data Selection. NeurIPS Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry
2024	A*	`conf`	Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content. EC Sarah H. Cen, Andrew Ilyas, Jennifer Allen, Hannah Li, Aleksander Madry
2024	J	`jnl`	Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content. CoRR Sarah H. Cen, Andrew Ilyas, Jennifer Allen, Hannah Li, Aleksander Madry
2024	A*	`conf`	User Strategization and Trustworthy Algorithms. EC Sarah H. Cen, Andrew Ilyas, Aleksander Madry
2023	A*	`conf`	FFCV: Accelerating Training by Removing Data Bottlenecks. CVPR Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry
2023	J	`jnl`	FFCV: Accelerating Training by Removing Data Bottlenecks. CoRR Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry
2023	A*	`conf`	ModelDiff: A Framework for Comparing Learning Algorithms. ICML Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry
2023	A*	`conf`	Raising the Cost of Malicious AI-Powered Image Editing. ICML Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry
2023	J	`jnl`	Raising the Cost of Malicious AI-Powered Image Editing. CoRR Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry
2023	A*	`conf`	Rethinking Backdoor Attacks. ICML Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry
2023	J	`jnl`	Rethinking Backdoor Attacks. CoRR Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry
2023	A*	`conf`	TRAK: Attributing Model Behavior at Scale. ICML Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry
2023	J	`jnl`	TRAK: Attributing Model Behavior at Scale. CoRR Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry
2023	J	`jnl`	User Strategization and Trustworthy Algorithms. CoRR Sarah H. Cen, Andrew Ilyas, Aleksander Madry
2023	A*	`conf`	What Makes a Good Fisherman? Linear Regression under Self-Selection Bias. STOC Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
2022	A*	`conf`	3DB: A Framework for Debugging Computer Vision Models. NeurIPS 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
2022	J	`jnl`	Datamodels: Predicting Predictions from Training Data. CoRR Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry
2022	A*	`conf`	Datamodels: Understanding Predictions with Data and Data with Predictions. ICML Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry
2022	A*	`conf`	Estimation of Standard Auction Models. EC Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
2022	J	`jnl`	Estimation of Standard Auction Models. CoRR Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
2022	J	`jnl`	ModelDiff: A Framework for Comparing Learning Algorithms. CoRR Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry
2022	J	`jnl`	What Makes A Good Fisherman? Linear Regression under Self-Selection Bias. CoRR Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Andrew Ilyas, Manolis Zampetakis
2022	J	`jnl`	When does Bias Transfer in Transfer Learning? CoRR Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry
2021	J	`jnl`	3DB: A Framework for Debugging Computer Vision Models. CoRR 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
2021	A*	`conf`	Noise or Signal: The Role of Image Backgrounds in Object Recognition. ICLR Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
2021	A*	`conf`	Unadversarial Examples: Designing Objects for Robust Vision. NeurIPS Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor
2020	A*	`conf`	A Closer Look at Deep Policy Gradients. ICLR Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
2020	A	`conf`	A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. AISTATS Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis
2020	A*	`conf`	Do Adversarially Robust ImageNet Models Transfer Better? NeurIPS Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry
2020	J	`jnl`	Do Adversarially Robust ImageNet Models Transfer Better? CoRR Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry
2020	A*	`conf`	From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. ICML Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry
2020	J	`jnl`	From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. CoRR Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry
2020	A*	`conf`	Identifying Statistical Bias in Dataset Replication. ICML Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
2020	J	`jnl`	Identifying Statistical Bias in Dataset Replication. CoRR Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
2020	J	`jnl`	Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO. CoRR Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
2020	A*	`conf`	Implementation Matters in Deep RL: A Case Study on PPO and TRPO. ICLR Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
2020	J	`jnl`	Noise or Signal: The Role of Image Backgrounds in Object Recognition. CoRR Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
2020	J	`jnl`	Unadversarial Examples: Designing Objects for Robust Vision. CoRR Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor
2019	A*	`conf`	Adversarial Examples Are Not Bugs, They Are Features. NeurIPS Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
2019	J	`jnl`	Adversarial Examples Are Not Bugs, They Are Features. CoRR Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
2019	J	`jnl`	Computer Vision with a Single (Robust) Classifier. CoRR Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Madry
2019	A*	`conf`	Image Synthesis with a Single (Robust) Classifier. NeurIPS Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
2019	J	`jnl`	Learning Perceptually-Aligned Representations via Adversarial Robustness. CoRR Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry
2019	—	`conf`	Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors. ICLR (Poster) Andrew Ilyas, Logan Engstrom, Aleksander Madry
2018	J	`jnl`	Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms? CoRR Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry
2018	A*	`conf`	Black-box Adversarial Attacks with Limited Queries and Information. ICML Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
2018	J	`jnl`	Black-box Adversarial Attacks with Limited Queries and Information. CoRR Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
2018	J	`jnl`	Evaluating and Understanding the Robustness of Adversarial Logit Pairing. CoRR Logan Engstrom, Andrew Ilyas, Anish Athalye
2018	A*	`conf`	Extracting Syntactical Patterns from Databases. ICDE Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez, Samuel Madden
2018	A*	`conf`	How Does Batch Normalization Help Optimization? NeurIPS Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018	J	`jnl`	How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift). CoRR Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018	J	`jnl`	Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors. CoRR Andrew Ilyas, Logan Engstrom, Aleksander Madry
2018	A*	`conf`	Synthesizing Robust Adversarial Examples. ICML Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok
2018	—	`conf`	Training GANs with Optimism. ICLR (Poster) Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
2017	J	`jnl`	Extracting Syntactic Patterns from Databases. CoRR Andrew Ilyas, Joana M. F. da Trindade, Raul Castro Fernandez, Samuel Madden
2017	J	`jnl`	Query-Efficient Black-box Adversarial Examples. CoRR Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
2017	J	`jnl`	Synthesizing Robust Adversarial Examples. CoRR Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok
2017	J	`jnl`	The Robust Manifold Defense: Adversarial Training using Generative Models. CoRR Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, Alexandros G. Dimakis
2017	J	`jnl`	Training GANs with Optimism. CoRR Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
2014	—	`conf`	MicroFilters: Harnessing twitter for disaster management. GHTC Andrew Ilyas