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ORCIDLiam Fowl
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2020 – today
- 2024
[i24]Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum:
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR abs/2403.16365 (2024)- 2023
- [c18]Beltrán Labrador, Guanlong Zhao, Ignacio López-Moreno, Angelo Scorza Scarpati, Liam Fowl, Quan Wang:
Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting. ICASSP 2023: 1-5 - [c17]Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein:
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. ICLR 2023 - [c16]Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. ICLR 2023 - 2022
- [c15]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CVPR Workshops 2022: 197-204 - [c14]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR 2022: 13689-13698 - [c13]Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. ICLR 2022 - [c12]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. ICML 2022: 23668-23684 - [c11]Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein:
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. NeurIPS 2022 - [i23]Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor:
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. CoRR abs/2201.00762 (2022) - [i22]Liam Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojtek Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. CoRR abs/2201.12675 (2022) - [i21]Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein:
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. CoRR abs/2202.00580 (2022) - [i20]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR abs/2203.08124 (2022) - [i19]Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein:
Poisons that are learned faster are more effective. CoRR abs/2204.08615 (2022) - [i18]Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein:
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning. CoRR abs/2210.09305 (2022) - [i17]Beltrán Labrador, Guanlong Zhao, Ignacio López-Moreno, Angelo Scorza Scarpati, Liam Fowl, Quan Wang:
Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting. CoRR abs/2211.06478 (2022) - 2021
- [c10]Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta:
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff. ICASSP 2021: 3855-3859 - [c9]Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. ICLR 2021 - [c8]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. NeurIPS 2021: 30339-30351 - [i16]Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein:
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. CoRR abs/2102.13624 (2021) - [i15]Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein:
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. CoRR abs/2103.02079 (2021) - [i14]Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein:
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. CoRR abs/2103.02683 (2021) - [i13]Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, Tom Goldstein:
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. CoRR abs/2106.08970 (2021) - [i12]Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojtek Czaja, Tom Goldstein:
Adversarial Examples Make Strong Poisons. CoRR abs/2106.10807 (2021) - [i11]Liam Fowl, Jonas Geiping, Wojtek Czaja, Micah Goldblum, Tom Goldstein:
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. CoRR abs/2110.13057 (2021) - 2020
- [c7]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. AAAI 2020: 3996-4003 - [c6]Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) 2020: 55-70 - [c5]Ahmed Abdelkader, Michael J. Curry
, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu:
Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP 2020: 3087-3091 - [c4]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. ICBINB@NeurIPS 2020: 87-97 - [c3]Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein:
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. ICML 2020: 3607-3616 - [c2]Micah Goldblum, Liam Fowl, Tom Goldstein:
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. NeurIPS 2020 - [c1]W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. NeurIPS 2020 - [i10]Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein:
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. CoRR abs/2002.06753 (2020) - [i9]W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. CoRR abs/2004.00225 (2020) - [i8]Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu:
Headless Horseman: Adversarial Attacks on Transfer Learning Models. CoRR abs/2004.09007 (2020) - [i7]Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein:
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. CoRR abs/2009.02276 (2020) - [i6]Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein:
Random Network Distillation as a Diversity Metric for Both Image and Text Generation. CoRR abs/2010.06715 (2020) - [i5]Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta:
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff. CoRR abs/2011.09527 (2020)
2010 – 2019
- 2019
- [i4]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. CoRR abs/1905.09747 (2019) - [i3]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization through Visualizations. CoRR abs/1906.03291 (2019) - [i2]Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Strong Baseline Defenses Against Clean-Label Poisoning Attacks. CoRR abs/1909.13374 (2019) - [i1]Micah Goldblum, Liam Fowl, Tom Goldstein:
Robust Few-Shot Learning with Adversarially Queried Meta-Learners. CoRR abs/1910.00982 (2019)
Coauthor Index
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