29 citations · 71 across the 48 of their papers we have counts for
50 papers
Test-Time Scalable AI-RAN: Inference Time Allocation for Cell-Free MIMO
Seonghoon Yoo, Sangwoo Park, Seok-Hwan Park +1
Artificial intelligence-enabled radio access networks (AI-RANs) are envisioned to consist of multiple AI-based modules, potentially developed independently by different vendors. In…
Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
Myoungjun Kim, Sangwoo Park, Yoseob Han +1
Differentially private federated fine-tuning of large models with LoRA suffers from aggregation error caused by LoRA's multiplicative structure, which is further amplified by DP no…
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…
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…
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…
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…
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning
Qiushuo Hou, Matteo Zecchin, Sangwoo Park +4
In modern wireless network architectures, such as O-RAN, artificial intelligence (AI)-based applications are deployed at intelligent controllers to carry out functionalities like s…
Reliable Sub-Nyquist Spectrum Sensing via Conformal Risk Control
Hyojin Lee, Sangwoo Park, Osvaldo Simeone +2
Detecting occupied subbands is a key task for wireless applications such as unlicensed spectrum access. Recently, detection methods were proposed that extract per-subband features…
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
Jiayi Huang, Sangwoo Park, Osvaldo Simeone
The application of artificial intelligence (AI) models in fields such as engineering is limited by the known difficulty of quantifying the reliability of an AI's decision. A well-c…
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
Jiechen Chen, Sangwoo Park, Petar Popovski +2
Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for…