Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Nguyen, Kim An"'
Autor:
Kevi, Eniko, Thang, Nguyen Kim
Matching problems have been widely studied in the research community, especially Ad-Auctions with many applications ranging from network design to advertising. Following the various advancements in machine learning, one natural question is whether cl
Externí odkaz:
http://arxiv.org/abs/2402.08701
Learning at the edges has become increasingly important as large quantities of data are continually generated locally. Among others, this paradigm requires algorithms that are simple (so that they can be executed by local devices), robust (again unce
Externí odkaz:
http://arxiv.org/abs/2402.02114
Autor:
Kevi, Enikő, Nguyen, Kim-Thang
Designing online algorithms with machine learning predictions is a recent technique beyond the worst-case paradigm for various practically relevant online problems (scheduling, caching, clustering, ski rental, etc.). While most previous learning-augm
Externí odkaz:
http://arxiv.org/abs/2312.14564
Decentralized learning has been studied intensively in recent years motivated by its wide applications in the context of federated learning. The majority of previous research focuses on the offline setting in which the objective function is static. H
Externí odkaz:
http://arxiv.org/abs/2210.16790
The design of decentralized learning algorithms is important in the fast-growing world in which data are distributed over participants with limited local computation resources and communication. In this direction, we propose an online algorithm minim
Externí odkaz:
http://arxiv.org/abs/2208.00522
Autor:
Thang, Nguyen Kim
Online bipartite matching has been extensively studied. In the unweighted setting, Karp et al. gave an optimal $(1 - 1/e)$-competitive randomized algorithm. In the weighted setting, optimal algorithms have been achieved only under assumptions on the
Externí odkaz:
http://arxiv.org/abs/2111.01718
Autor:
Thang, Nguyen Kim, Durr, Christoph
The domain of online algorithms with predictions has been extensively studied for different applications such as scheduling, caching (paging), clustering, ski rental, etc. Recently, Bamas et al., aiming for an unified method, have provided a primal-d
Externí odkaz:
http://arxiv.org/abs/2110.00391
Autor:
Thang, Nguyen Kim, Srivastav, Abhinav
In this paper, we study fundamental problems of maximizing DR-submodular continuous functions that have real-world applications in the domain of machine learning, economics, operations research and communication systems. It captures a subclass of non
Externí odkaz:
http://arxiv.org/abs/1909.11426
We study various discrete nonlinear combinatorial optimization problems in an online learning framework. In the first part, we address the question of whether there are negative results showing that getting a vanishing (or even vanishing approximate)
Externí odkaz:
http://arxiv.org/abs/1907.05944
Diminishing-returns (DR) submodular optimization is an important field with many real-world applications in machine learning, economics and communication systems. It captures a subclass of non-convex optimization that provides both practical and theo
Externí odkaz:
http://arxiv.org/abs/1905.09595