1 code implementation • 18 Jun 2023 • Lingda Wang, Savana Ammons, Vera Mikyoung Hur, Ryan L. Sriver, Zhizhen Zhao
Predicting sea surface temperature (SST) within the El Ni\~no-Southern Oscillation (ENSO) region has been extensively studied due to its significant influence on global temperature and precipitation patterns.
1 code implementation • 16 Jun 2022 • Lingda Wang, Zhizhen Zhao
This problem, with a variety of real-world applications, aims to recover the cluster structure and associated phase angles simultaneously.
no code implementations • 16 Feb 2022 • Longshaokan Wang, Lingda Wang, Mina Georgieva, Paulo Machado, Abinaya Ulagappa, Safwan Ahmed, Yan Lu, Arjun Bakshi, Farhad Ghassemi
Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities.
no code implementations • 10 Dec 2020 • Lingda Wang, Bingcong Li, Huozhi Zhou, Georgios B. Giannakis, Lav R. Varshney, Zhizhen Zhao
The second algorithm, \texttt{EXP3-LGC-IX}, is developed for a special class of problems, for which the regret is reduced to $\mathcal{O}(\sqrt{\alpha(G)dT\log{K}\log(KT)})$ for both directed as well as undirected feedback graphs.
no code implementations • 9 Dec 2020 • Bingcong Li, Lingda Wang, Georgios B. Giannakis, Zhizhen Zhao
Relying on no problem dependent parameters in the step sizes, the convergence rate of ExtraFW for general convex problems is shown to be ${\cal O}(\frac{1}{k})$, which is optimal in the sense of matching the lower bound on the number of solved FW subproblems.
no code implementations • 12 Sep 2019 • Lingda Wang, Huozhi Zhou, Bingcong Li, Lav R. Varshney, Zhizhen Zhao
Cascading bandit (CB) is a popular model for web search and online advertising, where an agent aims to learn the $K$ most attractive items out of a ground set of size $L$ during the interaction with a user.
no code implementations • 27 Aug 2019 • Huozhi Zhou, Lingda Wang, Lav R. Varshney, Ee-Peng Lim
Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps.
no code implementations • ICML 2020 • Bingcong Li, Lingda Wang, Georgios B. Giannakis
Then a simple yet effective means to adjust the number of iterations per inner loop is developed to enhance the merits of the proposed averaging schemes and BB step sizes.