Author

Atsushi Nitanda

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#machine learning Preprint Oct 2026

Learning Decision-Stump Thresholds in Context: Dynamics of Softmax Attention

Estimating a decision threshold requires locating observations near an unknown boundary. We study how gradient-based pretraining learns this statistical rule in a two-parameter softmax-attention model with a fixed feature and inequality direction. Pretraining uses labeled contexts and their true thresholds; a fresh thr...

H. Lệ, Jackie Lok, Atsushi Nitanda et al. · 0 citations

#machine learning Preprint Oct 2026

Diffusion Transformers are Provably Optimal In-context Generators

Generative foundation models are attracting interest for their ability to produce desired outputs from demonstrations given at inference time, without updating parameters. However, since a few demonstrations cannot uniquely identify the intended task, the challenge is how to learn and sample from an output distribution...

Guoji Fu, Tomoya Wakayama, Ryotaro Kawata et al. · 0 citations