3 citations · 4 across the 7 of their papers we have counts for
7 papers
Leveraging Bayesian Optimization for Array Shape Self-Calibration in Underwater DoA Estimation
Xin Gui, Tianang Li, Changjia Wang +3
Flexible sensing arrays are commonly used in underwater acoustic networks, but suppressed by unpredictable geometric deformations. Existing array shape self-calibration methods oft…
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…
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…
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction
Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
Black-box zero-th order optimization is a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a d…
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Yunchuan Zhang, Osvaldo Simeone, Sharu Theresa Jose +2
Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each can…
Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo
Yunchuan Zhang, Dongzhu Liu, Osvaldo Simeone
For engineering applications of artificial intelligence, Bayesian learning holds significant advantages over standard frequentist learning, including the capacity to quantify uncer…
Transfer Bayesian Meta-learning via Weighted Free Energy Minimization
Yunchuan Zhang, Sharu Theresa Jose, Osvaldo Simeone
Meta-learning optimizes the hyperparameters of a training procedure, such as its initialization, kernel, or learning rate, based on data sampled from a number of auxiliary tasks. A…