Search Results for author: Lingda Wang

Found 7 papers, 0 papers with code

Multi-Frequency Joint Community Detection and Phase Synchronization

no code implementations16 Jun 2022 Lingda Wang, Zhizhen Zhao

Numerical experiments indicate our proposed algorithms outperform state-of-the-art algorithms, in recovering community memberships and associated phases.

Community Detection Stochastic Block Model

Robust Nonparametric Distribution Forecast with Backtest-based Bootstrap and Adaptive Residual Selection

no code implementations16 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.

Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

no code implementations10 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.

Multi-Armed Bandits

Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem

no code implementations9 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.

Matrix Completion

Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits

no code implementations12 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.

A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits

no code implementations27 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.

Change Detection Multi-Armed Bandits

Almost Tune-Free Variance Reduction

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.

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