15 citations · 57 across the 29 of their papers we have counts for
5 papers · 1 filter
Optimal Scheduling Control in Fluid Models of General Input-Queued Switches
Yingdong Lu, Mark S. Squillante, Tonghoon Suk
Most of the early input-queued switch research focused on establishing throughput optimality of the max-weight scheduling policy, with some recent research showing that max-weight…
On Heavy-Traffic Optimal Scaling of -Weighted MaxWeight Scheduling in Input-Queued Switches
Yingdong Lu, Siva Theja Maguluri, Mark S. Squillante +1
We consider the optimal control of input-queued switches under a cost-weighted variant of MaxWeight scheduling, for which we establish theoretical properties that include showing t…
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framew…
Optimising capacity allocation in networks of stochastic loss systems: A functional-form approach
Brendan Patch, Mark S. Squillante, Peter M. Van de Ven
Motivated by a wide variety of applications, this paper introduces a general class of networks of stochastic loss systems in which congestion renders lost revenue due to customers…
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen +3
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important…