activity
20222025
most citedLearning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025

Near-Optimal Regret-Queue Length Tradeoff in Online Learning for Two-Sided Markets

Zixian Yang, Sushil Mahavir Varma, Lei Ying

We study a two-sided market, wherein, price-sensitive heterogeneous customers and servers arrive and join their respective queues. A compatible customer-server pair can then be mat…

math.OC2024

Learning-Based Pricing and Matching for Two-Sided Queues

Zixian Yang, Lei Ying

We consider a dynamic system with multiple types of customers and servers. Each type of waiting customer or server joins a separate queue, forming a bipartite graph with customer-s…

eess.SY2023

From Classification to Optimization: Slicing and Resource Management with TRACTOR

Joshua Groen, Zixian Yang, Divyadharshini Muruganandham +3

5G and beyond networks promise advancements in bandwidth, latency, and connectivity. The Open Radio Access Network (O-RAN) framework enhances flexibility through network slicing an…

cs.LG2023

Online Nonstochastic Control with Adversarial and Static Constraints

Xin Liu, Zixian Yang, Lei Ying

This paper studies online nonstochastic control problems with adversarial and static constraints. We propose online nonstochastic control algorithms that achieve both sublinear reg…

cs.LG2022★ 1 cited

Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB

Zixian Yang, R. Srikant, Lei Ying

Multi-server queueing systems are widely used models for job scheduling in machine learning, wireless networks, crowdsourcing, and healthcare systems. This paper considers a multi-…

cs.LG2022

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment

Zixian Yang, Xin Liu, Lei Ying

Multi-armed bandit (MAB) is a classic model for understanding the exploration-exploitation trade-off. The traditional MAB model for recommendation systems assumes the user stays in…