Liam Fowl

According to our database, Liam Fowl authored at least 27 papers between 2019 and 2024.

Collaborative distances:

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

2024

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion.

Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum

CoRR, 2024

2023

Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.

Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation.

Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting.

Beltrán Labrador, Guanlong Zhao, Ignacio López-Moreno, Angelo Scorza Scarpati, Liam Fowl, Quan Wang

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning.

Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein

CoRR, 2022

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning.

Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor

CoRR, 2022

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch.

Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein

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

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification.

Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2022

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models.

Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective.

Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Poisons that are learned faster are more effective.

Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release.

Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, 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

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors.

Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein

CoRR, 2021

Adversarial Examples Make Strong Poisons.

Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein

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

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.

Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 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

2020

Random Network Distillation as a Diversity Metric for Both Image and Text Generation.

Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein

CoRR, 2020

MetaPoison: Practical General-purpose Clean-label Data Poisoning.

W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

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

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach.

Micah Goldblum, Liam Fowl, Tom Goldstein

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

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks.

Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein

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

Understanding Generalization Through Visualizations.

W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

Headless Horseman: Adversarial Attacks on Transfer Learning Models.

Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Deep k-NN Defense Against Clean-Label Data Poisoning Attacks.

Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

Adversarially Robust Distillation.

Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Robust Few-Shot Learning with Adversarially Queried Meta-Learners.

Micah Goldblum, Liam Fowl, Tom Goldstein

CoRR, 2019

Strong Baseline Defenses Against Clean-Label Poisoning Attacks.

Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

CoRR, 2019