29 citations · 71 across the 48 of their papers we have counts for
11 papers · 1 filter
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…
On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization
Prabodh Katti, Houssem Sifaou, Sangwoo Park +2
On-device fine-tuning is a critical capability for edge AI systems, which must support adaptation to different agentic tasks under stringent memory constraints. Conventional backpr…
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…
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…
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…
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction
Seonghoon Yoo, Sangwoo Park, Petar Popovski +2
Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems, rely on artificial intelligence (AI)-powered applications running on controllers interfaced wit…
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…