Transformers achieve optimal in-context learning for regression

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 1 source

Researchers have developed a method for in-context learning in nonparametric regression using transformers. Their findings indicate that transformers can achieve minimax optimal convergence rates with significantly fewer parameters and pretraining sequences than previously thought. This is accomplished by enabling transformers to approximate local polynomial estimators through a kernel-weighted polynomial basis and gradient descent. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Demonstrates a more efficient approach to in-context learning, potentially reducing computational requirements for transformer-based regression tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for in-context learning with transformers. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 · Michelle Ching, Ioana Popescu, Nico Smith, Tianyi Ma, William G. Underwood, Richard J. Samworth · 2026-05-20 04:00

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers

arXiv:2601.15014v2 Announce Type: replace Abstract: We study in-context learning for nonparametric regression with $\alpha$-H\"older smooth regression functions, for some $\alpha>0$. We prove that, with $n$ in-context examples and $d$-dimensional regression covariates, a pretrain…

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Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers

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