Guillaume Leclerc

According to our database, Guillaume Leclerc authored at least 15 papers between 2016 and 2023.

Collaborative distances:

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

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

Adversarially trained neural representations may already be as robust as corresponding biological neural representations.

Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo

CoRR, 2022

Datamodels: Predicting Predictions from Training Data.

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

CoRR, 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

Adversarially trained neural representations are already as robust as biological neural representations.

Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo

Proceedings of the International Conference on Machine Learning, 2022

2020

Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy.

Aditya Saligrama, Guillaume Leclerc

CoRR, 2020

The Two Regimes of Deep Network Training.

Guillaume Leclerc, Aleksander Madry

CoRR, 2020

Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions.

Julian Klug, Guillaume Leclerc, Elisabeth Dirren, Maria Giulia Preti, Dimitri Van De Ville, Emmanuel Carrera

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019

SageDB: A Learned Database System.

Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

2018

Smallify: Learning Network Size while Training.

Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez, Tim Kraska, Samuel Madden

CoRR, 2018

2016

The Seamless Peer and Cloud Evolution Framework.

Guillaume Leclerc, Joshua Evan Auerbach, Giovanni Iacca, Dario Floreano

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016