Eitan Borgnia

According to our database, Eitan Borgnia authored at least 11 papers between 2021 and 2023.

Collaborative distances:

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

2023

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries.

Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

What do Vision Transformers Learn? A Visual Exploration.

Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein

CoRR, 2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2022

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking.

Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2022

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability.

Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein

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

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking.

Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

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

2021

Datasets for Studying Generalization from Easy to Hard Examples.

Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2021

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations.

Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2021

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks.

Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

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

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff.

Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta

Proceedings of the IEEE International Conference on Acoustics, 2021