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cs.LG2025
MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention
Can Yaras, Alec S. Xu, Pierre Abillama +2
Transformers have achieved state-of-the-art performance across various tasks, but suffer from a notable quadratic complexity in sequence length due to the attention mechanism. In t…
stat.ML2025★ 1 cited
Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective
Soo Min Kwon, Alec S. Xu, Can Yaras +2
The transformer's remarkable ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its strengths and limitations. However, a theor…
cs.LG2025
Linearly Separable Features in Shallow Nonlinear Networks: Width Scales Polynomially with Intrinsic Data Dimension
Alec S. Xu, Can Yaras, Peng Wang +1
Deep neural networks have attained remarkable success across diverse classification tasks. Recent empirical studies have shown that deep networks learn features that are linearly s…