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Autor: Cohen, Kfir M., Park, Sangwoo, Simeone, Osvaldo, Shamai, Shlomo

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a conventional point predictor to provide calibration guarantees. Generalizing conformal prediction (CP), with CRC, calibration is ensured for a set predictor that

Externí odkaz: http://arxiv.org/abs/2401.11974

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Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction

Autor: Cohen, Kfir M., Park, Sangwoo, Simeone, Osvaldo, Popovski, Petar, Shamai, Shlomo

Publikováno v: IEEE Signal Processing Letters, vol. 30, pp. 473-477, April 2023

The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The

Externí odkaz: http://arxiv.org/abs/2302.07675

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Calibrating AI Models for Wireless Communications via Conformal Prediction

Autor: Cohen, Kfir M., Park, Sangwoo, Simeone, Osvaldo, Shamai, Shlomo

When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A well-calibrated AI model is one that can reliably quantify the uncert

Externí odkaz: http://arxiv.org/abs/2212.07775

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Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction

Autor: Cohen, Kfir M., Park, Sangwoo, Simeone, Osvaldo, Shamai, Shlomo

Publikováno v: IEEE Transactions on Machine Learning in Communications and Networking, vol. 1, pp. 296-312, September 2023

AI tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabling to quantify their outputs uncertainty. While Bayesian learning can

Externí odkaz: http://arxiv.org/abs/2210.04621

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Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction

Autor: Park, Sangwoo, Cohen, Kfir M., Simeone, Osvaldo

Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions. Bayesian learning can improve calibration, but formal guarantees apply only under restrictive assumptions

Externí odkaz: http://arxiv.org/abs/2210.03067

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Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization

Autor: Cohen, Kfir M., Park, Sangwoo, Simeone, Osvaldo, Shamai, Shlomo

Publikováno v: IEEE Transactions on Signal Processing, vol. 70, pp. 5366-5380, 2022

Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in communication networks are adaptation and monitoring. Adaptation refers to the need to adjust the operation of an AI module depending on the current con

Externí odkaz: http://arxiv.org/abs/2108.00785

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