ML Anthology

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

Soyon Choi
AAAI 2025 pp. 29570-29572
doi:10.1609/AAAI.V39I28.35328 /aaai/2025/choi2025aaai-knowing/

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.

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Cite

Text

Choi. "Does Knowing More Make You Easier to Trick? Adversarial Robustness of Multi-Target Regression." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35328

Markdown

[Choi. "Does Knowing More Make You Easier to Trick? Adversarial Robustness of Multi-Target Regression." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/choi2025aaai-knowing/) doi:10.1609/AAAI.V39I28.35328

BibTeX

@inproceedings{choi2025aaai-knowing,
  title     = {{Does Knowing More Make You Easier to Trick? Adversarial Robustness of Multi-Target Regression}},
  author    = {Choi, Soyon},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29570-29572},
  doi       = {10.1609/AAAI.V39I28.35328},
  url       = {https://mlanthology.org/aaai/2025/choi2025aaai-knowing/}
}