2 citations · 2 across the 3 of their papers we have counts for
3 papers
math.OC2025
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
Luo Long, Coralia Cartis, Paz Fink Shustin
Bayesian optimisation (BO) is a standard approach for sample-efficient global optimisation of expensive black-box functions, yet its scalability to high dimensions remains challeng…
math.OC2024★ 2 cited
Dimensionality Reduction Techniques for Global Bayesian Optimisation
Luo Long, Coralia Cartis, Paz Fink Shustin
Bayesian Optimisation (BO) is a state-of-the-art global optimisation technique for black-box problems where derivative information is unavailable, and sample efficiency is crucial.…
cs.PF2024
Approximation Algorithms for Minimizing Congestion in Demand-Aware Networks
Wenkai Dai, Michael Dinitz, Klaus-Tycho Foerster +2
Emerging reconfigurable optical communication technologies allow to enhance datacenter topologies with demand-aware links optimized towards traffic patterns. This paper studies the…