3 citations · 8 across the 34 of their papers we have counts for
41 papers
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees
Jayadev Naram, Giuseppe Durisi, Matteo Zecchin +1
Automated decision makers, such as AI agents that control robots or network devices, must often satisfy hard cost constraints while maintaining competitive performance. In such set…
Conformal Individual Treatment Effect Estimation under Networked Interference
Matteo Zecchin, Osvaldo Simeone
Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-inte…
Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks
Abdessamed Qchohi, Jessica Moysen Cortes, Matteo Zecchin
Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take th…
Online Conformal Prediction with Corrupted Feedback
Bowen Wang, Matteo Zecchin, Osvaldo Simeone
Modern artificial intelligence systems require calibrated uncertainty estimates that remain reliable in sequential and non-stationary environments. Online conformal prediction (OCP…
Federated Martingale Posterior Samping
Boning Zhang, Matteo Zecchin, Mingzhao Guo +2
Federated Bayesian neural networks require fixing a prior on the model parameters, which is notoriously difficult, and misspecification of this prior can severely degrade accuracy…
Universal Sequential Changepoint Detection of Quantum Observables via Classical Shadows
Matteo Zecchin, Osvaldo Simeone, Aaditya Ramdas
We study sequential quantum changepoint detection in settings where the pre- and post-change regimes are specified through constraints on the expectation values of a finite set of…
Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems
Qiushuo Hou, Sangwoo Park, Matteo Zecchin +4
Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretat…
Distributed Dynamic Associative Memory via Online Convex Optimization
Bowen Wang, Matteo Zecchin, Osvaldo Simeone
An associative memory (AM) enables cue-response recall, and it has recently been recognized as a key mechanism underlying modern neural architectures such as Transformers. In this…
Prediction-Powered Communication with Distortion Guarantees
Matteo Zecchin, Unnikrishnan Kunnath Ganesan, Giuseppe Durisi +2
The development of 6G wireless systems is taking place alongside the development of increasingly intelligent wireless devices and network nodes. The changing technological landscap…
Distributed Associative Memory via Online Convex Optimization
Bowen Wang, Matteo Zecchin, Osvaldo Simeone
An associative memory (AM) enables cue-response recall, and associative memorization has recently been noted to underlie the operation of modern neural architectures such as Transf…
Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
Amirmohammad Farzaneh, Matteo Zecchin, Osvaldo Simeone
This work addresses the problem of constructing reliable prediction intervals for individual counterfactual outcomes. Existing conformal counterfactual inference (CCI) methods prov…
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
Matteo Zecchin, Tomer Raviv, Dileep Kalathil +3
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. L…
Turbo-ICL: In-Context Learning-Based Turbo Equalization
Zihang Song, Matteo Zecchin, Bipin Rajendran +1
This paper introduces a novel in-context learning (ICL) framework, inspired by large language models (LLMs), for soft-input soft-output channel equalization in coded multiple-input…
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees
Sangwoo Park, Matteo Zecchin, Osvaldo Simeone
Selecting artificial intelligence (AI) models, such as large language models (LLMs), from multiple candidates requires accurate performance estimation. This is ideally achieved thr…
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading
Qiushuo Hou, Sangwoo Park, Matteo Zecchin +3
Consider an edge computing setting in which a user submits queries for the solution of a linear system to an edge processor, which is subject to time-varying computing availability…
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…
Online Conformal Compression for Zero-Delay Communication with Distortion Guarantees
Unnikrishnan Kunnath Ganesan, Giuseppe Durisi, Matteo Zecchin +2
We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression s…
Mirror Online Conformal Prediction with Intermittent Feedback
Bowen Wang, Matteo Zecchin, Osvaldo Simeone
Online conformal prediction enables the runtime calibration of a pre-trained artificial intelligence model using feedback on its performance. Calibration is achieved through set pr…
Generalization and Informativeness of Weighted Conformal Risk Control Under Covariate Shift
Matteo Zecchin, Fredrik Hellström, Sangwoo Park +2
Predictive models are often required to produce reliable predictions under statistical conditions that are not matched to the training data. A common type of training-testing misma…
Robust Bayesian Optimization via Localized Online Conformal Prediction
Dongwon Kim, Matteo Zecchin, Sangwoo Park +2
Bayesian optimization (BO) is a sequential approach for optimizing black-box objective functions using zeroth-order noisy observations. In BO, Gaussian processes (GPs) are employed…
In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models
Zihang Song, Matteo Zecchin, Bipin Rajendran +1
Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done…
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,…
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone
We introduce adaptive learn-then-test (aLTT), an efficient hyperparameter selection procedure that provides finite-sample statistical guarantees on the population risk of AI models…
Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
Meiyi Zhu, Matteo Zecchin, Sangwoo Park +4
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication const…