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Transformers are Provably Optimal In-context Estimators for Wireless Communications
Published in AISTATS, 2025
This paper introduces the concept of in-context estimation (ICE), where pre-trained transformers adapt to new tasks by leveraging limited prompts without explicit optimization. It proves that single-layer softmax attention transformers (SATs) can optimally solve ICE problems for a subclass of cases and demonstrates that multi-layer transformers efficiently handle broader ICE problems, outperforming standard approaches. The study highlights that transformers achieve near-optimal performance with minimal context examples, rivaling estimators with perfect knowledge of the latent context.
Recommended citation: Vishnu Teja Kunde, Vicram Rajagopalan, Chandra Shekhara Kaushik Valmeekam, Krishna Narayanan, Srinivas Shakkottai, Dileep Kalathil, and Jean-Francois Chamberland. "Transformers are Provably Optimal In-context Estimators for Wireless Communications." arXiv preprint, arXiv:2311.00226, 2025
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teaching
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Workshop, University 1, Department, 2015
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