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Pedro Sandoval Segura
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2020 – today
- 2023
- [c6]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein:
What Can We Learn from Unlearnable Datasets? NeurIPS 2023 - [i11]Pedro Sandoval Segura, Jonas Geiping, Tom Goldstein:
JPEG Compressed Images Can Bypass Protections Against AI Editing. CoRR abs/2304.02234 (2023) - [i10]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein:
What Can We Learn from Unlearnable Datasets? CoRR abs/2305.19254 (2023) - [i9]Vasu Singla, Pedro Sandoval Segura, Micah Goldblum, Jonas Geiping, Tom Goldstein:
A Simple and Efficient Baseline for Data Attribution on Images. CoRR abs/2311.03386 (2023) - 2022
- [c5]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 - [c4]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs:
Autoregressive Perturbations for Data Poisoning. NeurIPS 2022 - [i8]Pedro Sandoval Segura, Wallace Lawson:
AutoProtoNet: Interpretability for Prototypical Networks. CoRR abs/2204.00929 (2022) - [i7]Pedro Sandoval Segura:
Adversarially robust segmentation models learn perceptually-aligned gradients. CoRR abs/2204.01099 (2022) - [i6]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) - [i5]Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David W. Jacobs:
Autoregressive Perturbations for Data Poisoning. CoRR abs/2206.03693 (2022) - 2020
- [c3]Daniel Bashir, George D. Montañez, Sonia Sehra, Pedro Sandoval Segura, Julius Lauw:
An Information-Theoretic Perspective on Overfitting and Underfitting. Australasian Conference on Artificial Intelligence 2020: 347-358 - [c2]Pedro Sandoval Segura, Julius Lauw, Daniel Bashir, Kinjal Shah, Sonia Sehra, Dominique Macias, George D. Montañez:
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity. ICAART (2) 2020: 980-986 - [i4]Daniel Bashir, George D. Montañez, Sonia Sehra, Pedro Sandoval Segura, Julius Lauw:
An Information-Theoretic Perspective on Overfitting and Underfitting. CoRR abs/2010.06076 (2020)
2010 – 2019
- 2019
- [c1]Mehdi Drissi, Pedro Sandoval Segura, Vivaswat Ojha, Julie Medero
:
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector. SemEval@NAACL-HLT 2019: 962-966 - [i3]Mehdi Drissi, Pedro Sandoval Segura, Vivaswat Ojha, Julie Medero:
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector. CoRR abs/1905.01962 (2019) - [i2]Pedro Sandoval Segura, Julius Lauw, Daniel Bashir, Kinjal Shah, Sonia Sehra, Dominique Macias, George D. Montañez:
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity. CoRR abs/1912.10597 (2019) - 2018
- [i1]Mehdi Drissi, Olivia Watkins, Aditya Khant
, Vivaswat Ojha, Pedro Sandoval Segura, Rakia Segev, Eric Weiner, Robert Keller:
Program Language Translation Using a Grammar-Driven Tree-to-Tree Model. CoRR abs/1807.01784 (2018)
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