Search Results for author: Shuoguang Yang

Found 12 papers, 1 papers with code

Federated Multi-Level Optimization over Decentralized Networks

no code implementations10 Oct 2023 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, such as meta-learning, multi-player games, reinforcement learning, and nested composition optimization.

Distributed Optimization Meta-Learning +1

Online Linearized LASSO

no code implementations11 Nov 2022 Shuoguang Yang, Yuhao Yan, Xiuneng Zhu, Qiang Sun

Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model.

regression Variable Selection

Stochastic Compositional Optimization with Compositional Constraints

no code implementations9 Sep 2022 Shuoguang Yang, Zhe Zhang, Ethan X. Fang

Stochastic compositional optimization (SCO) has attracted considerable attention because of its broad applicability to important real-world problems.


Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks

no code implementations22 Jun 2022 Shuoguang Yang, Xuezhou Zhang, Mengdi Wang

This paper studies the problem of distributed bilevel optimization over a network where agents can only communicate with neighbors, including examples from multi-task, multi-agent learning and federated learning.

Bilevel Optimization Federated Learning +3

Information-Directed Selection for Top-Two Algorithms

1 code implementation24 May 2022 Wei You, Chao Qin, ZiHao Wang, Shuoguang Yang

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 allocating measurement effort.

Multi-Armed Bandits Thompson Sampling +1

Data-Driven Minimax Optimization with Expectation Constraints

no code implementations16 Feb 2022 Shuoguang Yang, Xudong Li, Guanghui Lan

We propose a class of efficient primal-dual algorithms to tackle the minimax expectation-constrained problem, and show that our algorithms converge at the optimal rate of $\mathcal{O}(\frac{1}{\sqrt{N}})$.

Bridging Adversarial and Nonstationary Multi-armed Bandit

no code implementations5 Jan 2022 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

no code implementations6 Sep 2021 Shuoguang Yang, Van-Anh Truong

We propose a detailed analysis of the online-learning problem for Independent Cascade (IC) models under node-level feedback.

Revenue Maximization and Learning in Products Ranking

no code implementations7 Dec 2020 Ningyuan Chen, Anran Li, Shuoguang Yang

When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret relative to the approximation algorithm, despite the censoring of information: the attention span of a customer who purchases an item is not observable.

Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence

no code implementations8 Nov 2019 Shuoguang Yang, Shatian Wang, Van-Anh Truong

We show that in these models, the expected positive influence spread is a monotone submodular function of the seed set.

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