activity
20192023
most citedMulti-Frequency Joint Community Detection and Phase Synchronization

3 citations · 6 across the 6 of their papers we have counts for

collaborators

9 papers

physics.ao-ph2023

Convolutional GRU Network for Seasonal Prediction of the El Niño-Southern Oscillation

Lingda Wang, Savana Ammons, Vera Mikyoung Hur +2

Predicting sea surface temperature (SST) within the El Niño-Southern Oscillation (ENSO) region has been extensively studied due to its significant influence on global temperature a…

cs.SI2022★ 3 cited

Multi-Frequency Joint Community Detection and Phase Synchronization

Lingda Wang, Zhizhen Zhao

This paper studies the joint community detection and phase synchronization problem on the \textit{stochastic block model with relative phase}, where each node is associated with an…

stat.ML2022

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

Longshaokan Wang, Lingda Wang, Mina Georgieva +6

Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is c…

math.OC2020★ 1 cited

Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem

Bingcong Li, Lingda Wang, Georgios B. Giannakis +1

Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of Ex…

cs.LG2020★ 1 cited

Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

Lingda Wang, Bingcong Li, Huozhi Zhou +3

This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: \emp…

cs.LG2019

Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits

Lingda Wang, Huozhi Zhou, Bingcong Li +2

Cascading bandit (CB) is a popular model for web search and online advertising, where an agent aims to learn the most attractive items out of a ground set of size during th…