4 citations · 5 across the 13 of their papers we have counts for
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Iterative Randomized Rounding for Better-Than-2 k-Median Approximation
Jarosław Byrka, Yuhao Guo, Yang Hu +5
We study the classic -median problem and refine the iterative rounding framework recently introduced by Byrka, Guo, Hu, Li, Wan, and Wang [FOCS'26], which yields a $(2+\varepsil…
Pure Tail Constraints for Online Problems
Mateusz Basiak, Marcin Bienkowski, Yongho Shin +1
Controlling tail risk is an important objective in online optimization, and recently it has been studied in the context of competitive analysis. Continuing this line of research, w…
Parsimonious Learning-Augmented Online Metric Matching
Yongho Shin, Phanu Vajanopath
Learning-augmented algorithms have received significant attention in recent years, particularly in the context of online optimization. Motivated by the high computational cost of g…
Optimal Learning-Augmented Algorithm for Online Bidding
Changyeol Lee, Dahoon Lee, Jongseo Lee +2
Recent advances in machine learning have spurred significant interest in learning-augmented algorithms, particularly for online optimization. A growing body of work has studied onl…
Servicing Matched Client Pairs with Facilities
Fateme Abbasi, Martin Böhm, Jarosław Byrka +2
We study Facility Location with Matching, a Facility Location problem where, given additional information about which pair of clients is compatible to be matched, we need to match…