29 citations · 71 across the 48 of their papers we have counts for
10 papers · 1 filter
Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone
In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirement…
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gai…
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Meiyi Zhu, Matteo Zecchin, Sangwoo Park +3
In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical inf…
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction
Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
Black-box zero-th order optimization is a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a d…
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in ch…
Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics. The energy consumption of an SNN depends on the number of spikes exchanged betwee…
Calibration-Aware Bayesian Learning
Jiayi Huang, Sangwoo Park, Osvaldo Simeone
Deep learning models, including modern systems like large language models, are well known to offer unreliable estimates of the uncertainty of their decisions. In order to improve t…
Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +2
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 broa…
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning
Sangwoo Park, Osvaldo Simeone
In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees. Quantum models implement implicit probab…
Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +1
Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs…