5 papers
Kernel Autoresearch for Open-Ended Model Discovery
Richard Cornelius Suwandi, Feng Yin, Kevin Murphy
Kernels encode the inductive bias of a wide range of machine learning models, yet automated kernel design faces a fundamental dilemma. A fixed grammar of base kernels and operators…
GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimization
Richard Cornelius Suwandi, Feng Yin
Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learning and scientific discovery. While local Bayesian optimization mitiga…
PEMNet: Towards Autonomous and Enhanced Environment-Aware Mobile Networks
Lei Li, Yanqing Xu, Ye Xue +4
With 5G deployment and the evolution toward 6G, mobile networks must make decisions in highly dynamic environments under strict latency, energy, and spectrum constraints. Achieving…
Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
Richard Cornelius Suwandi, Feng Yin, Juntao Wang +3
The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation…
Learning to Gridize: Segment Physical World by Wireless Communication Channel
Juntao Wang, Feng Yin, Tian Ding +3
Gridization, the process of partitioning space into grids where users share similar channel characteristics, serves as a fundamental prerequisite for efficient large-scale network…