15 citations · 29 across the 41 of their papers we have counts for
19 papers · 1 filter
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…
Localized Adaptive Risk Control
Matteo Zecchin, Osvaldo Simeone
Adaptive Risk Control (ARC) is an online calibration strategy based on set prediction that offers worst-case deterministic long-term risk control, as well as statistical marginal c…
Cell-Free Multi-User MIMO Equalization via In-Context Learning
Matteo Zecchin, Kai Yu, Osvaldo Simeone
Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new…
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…
Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functio…
Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis
Petros Georgiou, Sharu Theresa Jose, Osvaldo Simeone
In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train t…
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks
Meiyi Zhu, Caili Guo, Chunyan Feng +1
In a membership inference attack (MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to…
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning
Eslam Eldeeb, Houssem Sifaou, Osvaldo Simeone +2
Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in…
Generalization and Informativeness of Conformal Prediction
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone +1
The safe integration of machine learning modules in decision-making processes hinges on their ability to quantify uncertainty. A popular technique to achieve this goal is conformal…
Cross-Validation Conformal Risk Control
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +1
Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a conventional point predictor to provide calibration guarantees. Generalizing conformal pred…
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
Mingzhao Guo, Dongzhu Liu, Osvaldo Simeone +1
This paper presents a novel gradient compression method for federated learning (FL) in wireless systems. The proposed method centers on a low-rank matrix factorization strategy for…