15 citations · 29 across the 41 of their papers we have counts for
47 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…
Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data
Zhenyu Tao, Wei Xu, Xiaohu You +2
Digital twins (DTs) and learned world models are increasingly used to generate synthetic data that augment the scarce real datasets available for training artificial intelligence (…
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…
Post-Selection Distributional Model Evaluation
Amirmohammad Farzaneh, Osvaldo Simeone
Formal model evaluation methods typically certify that a model satisfies a prescribed target key performance indicator (KPI) level. However, in many applications, the relevant targ…
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…
Reliable Inference in Edge-Cloud Model Cascades via Conformal Alignment
Jiayi Huang, Sangwoo Park, Nicola Paoletti +1
Edge intelligence enables low-latency inference via compact on-device models, but assuring reliability remains challenging. We study edge-cloud cascades that must preserve conditio…
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…
Optimizing In-Context Learning for Efficient Full Conformal Prediction
Weicao Deng, Sangwoo Park, Min Li +1
Reliable uncertainty quantification is critical for trustworthy AI. Conformal Prediction (CP) provides prediction sets with distribution-free coverage guarantees, but its two main…
Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration
Seonghoon Yoo, Houssem Sifaou, Sangwoo Park +2
Wireless indoor localization using predictive models with received signal strength information (RSSI) requires proper calibration for reliable position estimates. One remedy is to…
How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
Clement Ruah, Houssem Sifaou, Osvaldo Simeone +1
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and avai…
Conformal Robust Beamforming via Generative Channel Models
Xin Su, Qiushuo Hou, Ruisi He +1
Traditional approaches to outage-constrained beamforming optimization rely on statistical assumptions about channel distributions and estimation errors. However, the resulting outa…
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…
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…
Distilling Calibration via Conformalized Credal Inference
Jiayi Huang, Sangwoo Park, Nicola Paoletti +1
Deploying artificial intelligence (AI) models on edge devices involves a delicate balance between meeting stringent complexity constraints, such as limited memory and energy resour…
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…
Transferable Multi-Fidelity Bayesian Optimization for Radio Resource Management
Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
Radio resource allocation often calls for the optimization of black-box objective functions whose evaluation is expensive in real-world deployments. Conventional optimization metho…
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…