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Transformers are Provably Optimal In-context Estimators for Wireless Communications

1 November 2023

Vishnu Teja Kunde

Vicram Rajagopalan

Chandra Shekhara Kaushik Valmeekam

Papers citing "Transformers are Provably Optimal In-context Estimators for Wireless Communications"

33 / 33 papers shown

Title
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory Matteo Zecchin Tomer Raviv D. Kalathil Krishna R. Narayanan Nir Shlezinger Osvaldo Simeone 21 0 0 18 Jun 2025
Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization Zirui Chen Zhaoyang Zhang Ziqing Xing Ridong Li Zhaohui Yang Richeng Jin Chongwen Huang YuZhi Yang Mérouane Debbah 91 1 0 09 Apr 2025
Decision Feedback In-Context Learning for Wireless Symbol Detection Li Fan Jing Yang Jing Yang Cong Shen 136 0 0 20 Mar 2025
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches Atik Faysal Mohammad Rostami Taha Boushine Reihaneh Gh. Roshan Huaxia Wang Nikhil Muralidhar 68 1 0 25 Feb 2025
Decision Feedback In-Context Symbol Detection over Block-Fading Channels Li Fan Jing Yang Cong Shen 127 1 0 12 Nov 2024
In-Context Learning with Representations: Contextual Generalization of Trained Transformers Tong Yang Yu Huang Yingbin Liang Yuejie Chi MLT 115 12 0 19 Aug 2024
Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning Subhojyoti Mukherjee Josiah P. Hanna Qiaomin Xie Robert Nowak 270 2 0 07 Jun 2024
Cell-Free Multi-User MIMO Equalization via In-Context Learning Matteo Zecchin Kai Yu Osvaldo Simeone 88 9 0 08 Apr 2024
Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality Siyu Chen Heejune Sheen Tianhao Wang Zhuoran Yang MLT 93 46 0 29 Feb 2024
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness Liam Collins Advait Parulekar Aryan Mokhtari Sujay Sanghavi Sanjay Shakkottai MLT 53 15 0 18 Feb 2024
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models Matteo Zecchin Kai Yu Osvaldo Simeone 93 12 0 10 Nov 2023
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations Tianyu Guo Wei Hu Song Mei Huan Wang Caiming Xiong Silvio Savarese Yu Bai 109 60 0 16 Oct 2023
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? Jingfeng Wu Difan Zou Zixiang Chen Vladimir Braverman Quanquan Gu Peter L. Bartlett 206 61 0 12 Oct 2023
In-Context Convergence of Transformers Yu Huang Yuan Cheng Yingbin Liang MLT 121 73 0 08 Oct 2023
One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention Arvind V. Mahankali Tatsunori B. Hashimoto Tengyu Ma MLT 87 102 0 07 Jul 2023
Supervised Pretraining Can Learn In-Context Reinforcement Learning Jonathan Lee Annie Xie Aldo Pacchiano Yash Chandak Chelsea Finn Ofir Nachum Emma Brunskill OffRL 126 86 0 26 Jun 2023
Trained Transformers Learn Linear Models In-Context Ruiqi Zhang Spencer Frei Peter L. Bartlett 114 207 0 16 Jun 2023
In-Context Learning through the Bayesian Prism Madhuri Panwar Kabir Ahuja Navin Goyal BDL 93 48 0 08 Jun 2023
Transformers learn to implement preconditioned gradient descent for in-context learning Kwangjun Ahn Xiang Cheng Hadi Daneshmand S. Sra ODL 104 176 0 01 Jun 2023
Transformers learn in-context by gradient descent J. Oswald Eyvind Niklasson E. Randazzo João Sacramento A. Mordvintsev A. Zhmoginov Max Vladymyrov MLT 181 497 0 15 Dec 2022
What learning algorithm is in-context learning? Investigations with linear models Ekin Akyürek Dale Schuurmans Jacob Andreas Tengyu Ma Denny Zhou 165 493 0 28 Nov 2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes Shivam Garg Dimitris Tsipras Percy Liang Gregory Valiant 191 514 0 01 Aug 2022
Variational Autoencoder Leveraged MMSE Channel Estimation Michael Baur B. Fesl M. Koller Wolfgang Utschick 66 20 0 11 May 2022
Data Distributional Properties Drive Emergent In-Context Learning in Transformers Stephanie C. Y. Chan Adam Santoro Andrew Kyle Lampinen Jane X. Wang Aaditya K. Singh Pierre Harvey Richemond J. Mcclelland Felix Hill 245 266 0 22 Apr 2022
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Sewon Min Xinxi Lyu Ari Holtzman Mikel Artetxe M. Lewis Hannaneh Hajishirzi Luke Zettlemoyer LLMAG LRM 200 1,507 0 25 Feb 2022
An Explanation of In-context Learning as Implicit Bayesian Inference Sang Michael Xie Aditi Raghunathan Percy Liang Tengyu Ma ReLM BDL VPVLM LRM 336 764 0 03 Nov 2021
MetaICL: Learning to Learn In Context Sewon Min M. Lewis Luke Zettlemoyer Hannaneh Hajishirzi LRM 264 493 0 29 Oct 2021
End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication Fayçal Ait Aoudia J. Hoydis 116 94 0 11 Sep 2020
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms Yanna Bai Wei Chen Jie Chen Weisi Guo 123 70 0 27 Jul 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 1.3K 42,754 0 28 May 2020
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders Avi Caciularu D. Burshtein 78 50 0 21 May 2019
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 1.1K 133,599 0 12 Jun 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.7K 150,708 0 22 Dec 2014