Does Knowing More Make You Easier to Trick? Adversarial Robustness of Multi-Target Regression

Following the rapid rise of deep learning (DL) and generative artificial intelligence (GenAI), it is imperative that we gain a better understanding of how these machine learning (ML) systems actually learn. What information are DL models retaining from the training data? What reasoning capabilities do these models have? In my proposed project, I aim to tackle these pressing questions through use of an adversarial lens.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i28.35328
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Does Knowing More Make You Easier to Trick? Adversarial Robustness of Multi-Target Regression

S. Choi
Proceedings of the AAAI Conference on Artificial Intelligence
Adversarial Robustness in Machine Learning
article

Does Knowing More Make You Easier to Trick? Adversarial Robustness of Multi-Target Regression

S. Choi
article en

Abstract

Following the rapid rise of deep learning (DL) and generative artificial intelligence (GenAI), it is imperative that we gain a better understanding of how these machine learning (ML) systems actually learn. What information are DL models retaining from the training data? What reasoning capabilities do these models have? In my proposed project, I aim to tackle these pressing questions through use of an adversarial lens.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(28)
Amherst College (US)
Openalex Percentile: Top 73%
Adversarial Robustness in Machine Learning
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