15 citations · 29 across the 41 of their papers we have counts for
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Decision Making Needs Uncertainty Quantification [Lecture Notes]
Osvaldo Simeone
Many signal processing systems ultimately exist to act. Whenever the state variable that determines the action to be taken by a decision maker, or agent, is uncertain, the way that…
Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
Osvaldo Simeone, Sangwoo Park, Matteo Zecchin
AI is poised to revolutionize telecommunication networks by boosting efficiency, automation, and decision-making. However, the black-box nature of most AI models introduces substan…
What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems
Qiushuo Hou, Sangwoo Park, Matteo Zecchin +3
In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short,…
Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO
Sangwoo Park, Ahmet Hasim Gokceoglu, Li Wang +1
The conventional approach to the fronthaul design for cell-free massive MIMO system follows the compress-and-precode (CP) paradigm. Accordingly, encoded bits and precoding coeffici…
Quantile Learn-Then-Test: Quantile-Based Risk Control for Hyperparameter Optimization
Amirmohammad Farzaneh, Sangwoo Park, Osvaldo Simeone
The increasing adoption of Artificial Intelligence (AI) in engineering problems calls for the development of calibration methods capable of offering robust statistical reliability…
Cell-Free Multi-User MIMO Equalization via In-Context Learning
Matteo Zecchin, Kai Yu, Osvaldo Simeone
Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new…
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks
Meiyi Zhu, Caili Guo, Chunyan Feng +1
In a membership inference attack (MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to…
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
Mingzhao Guo, Dongzhu Liu, Osvaldo Simeone +1
This paper presents a novel gradient compression method for federated learning (FL) in wireless systems. The proposed method centers on a low-rank matrix factorization strategy for…
Optimizing Information Freshness over a Channel that Wears Out
George J. Stamatakis, Osvaldo Simeone, Nikolaos Pappas
A sensor samples and transmits status updates to a destination through a wireless channel that wears out over time and with every use. At each time slot, the sensor can decide to s…
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models
Matteo Zecchin, Kai Yu, Osvaldo Simeone
Large pre-trained sequence models, such as transformer-based architectures, have been recently shown to have the capacity to carry out in-context learning (ICL). In ICL, a decision…
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
Haifeng Wen, Hong Xing, Osvaldo Simeone
Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading…
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…