collaborators

20 papers

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

Federated Martingale Posterior Samping

Boning Zhang, Matteo Zecchin, Mingzhao Guo +2

Federated Bayesian neural networks require fixing a prior on the model parameters together with a likelihood. Eliciting meaningful priors on the weight space of modern overparamete…

eess.SP2026

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…

cs.LG2026

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…

cs.LG2026

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…