12 citations · 40 across the 13 of their papers we have counts for
4 papers · 1 filter
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility
Rajdeep Haldar, Yue Xing, Qifan Song +1
Recent works have shown theoretically and empirically that redundant data dimensions are a source of adversarial vulnerability. However, the inverse doesn't seem to hold in practic…
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
Rajdeep Haldar, Yue Xing, Qifan Song
The existence of adversarial attacks on machine learning models imperceptible to a human is still quite a mystery from a theoretical perspective. In this work, we introduce two not…
Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
Yue Xing, Xiaofeng Lin, Chenheng Xu +3
Large language models (LLMs) are powerful models that can learn concepts at the inference stage via in-context learning (ICL). While theoretical studies, e.g., \cite{zhang2023train…
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Yue Xing, Xiaofeng Lin, Qifan Song +3
Pre-training is known to generate universal representations for downstream tasks in large-scale deep learning such as large language models. Existing literature, e.g., \cite{kim202…