7 citations · 30 across the 14 of their papers we have counts for
14 papers
Online Generalized Sparse Regression: How Does Overparametrization Help?
Shuoguang Yang, Qiang Sun
Regularized sparse regression has been extensively studied in the offline setting, but online formulation remains relatively under-explored. This gap stems from four key challenges…
Federated Multi-Level Optimization over Decentralized Networks
Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, s…
Online Linearized LASSO
Shuoguang Yang, Yuhao Yan, Xiuneng Zhu +1
Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing ap…
Stochastic Compositional Optimization with Compositional Constraints
Shuoguang Yang, Wei You, Zhe Zhang +1
Stochastic compositional optimization (SCO) has attracted considerable attention because of its broad applicability to important real-world problems. However, existing works on SCO…
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
Bilevel optimization have gained growing interests, with numerous applications found in meta learning, minimax games, reinforcement learning, and nested composition optimization. T…
Information-Directed Selection for Top-Two Algorithms
Wei You, Chao Qin, Zihao Wang +1
We consider the best-k-arm identification problem for multi-armed bandits, where the objective is to select the exact set of k arms with the highest mean rewards by sequentially al…
Data-Driven Minimax Optimization with Expectation Constraints
Shuoguang Yang, Xudong Li, Guanghui Lan
Attention to data-driven optimization approaches, including the well-known stochastic gradient descent method, has grown significantly over recent decades, but data-driven constrai…
Bridging Adversarial and Nonstationary Multi-armed Bandit
Ningyuan Chen, Shuoguang Yang, Hailun Zhang
In the multi-armed bandit framework, there are two formulations that are commonly employed to handle time-varying reward distributions: adversarial bandit and nonstationary bandit.…
Online Learning of Independent Cascade Models with Node-level Feedback
Shuoguang Yang, Van-Anh Truong
We propose a detailed analysis of the online-learning problem for Independent Cascade (IC) models under node-level feedback. These models have widespread applications in modern soc…
Revenue Maximization and Learning in Products Ranking
Ningyuan Chen, Anran Li, Shuoguang Yang
We consider the revenue maximization problem for an online retailer who plans to display in order a set of products differing in their prices and qualities. Consumers have attentio…
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization
Shatian Wang, Shuoguang Yang, Zhen Xu +1
We propose a cumulative oversampling (CO) method for online learning. Our key idea is to sample parameter estimations from the updated belief space once in each round (similar to T…
Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence
Shuoguang Yang, Shatian Wang, Van-Anh Truong
Problem definition: Corporate brands, grassroots activists, and ordinary citizens all routinely employ Word-of-mouth (WoM) diffusion to promote products and instigate social change…
Multi-Level Stochastic Gradient Methods for Nested Composition Optimization
Shuoguang Yang, Mengdi Wang, Ethan X. Fang
Stochastic gradient methods are scalable for solving large-scale optimization problems that involve empirical expectations of loss functions. Existing results mainly apply to optim…
Robust linear control of nonconvex battery operation in transmission systems
Daniel Bienstock, Carsten Matke, Gonzalo Munoz +1
We describe a robust multiperiod transmission plan- ning model including renewables and batteries, where battery output is used to partly offset renewable output deviations from fo…