10 citations · 12 across the 5 of their papers we have counts for
5 papers
Towards VM Rescheduling Optimization Through Deep Reinforcement Learning
Xianzhong Ding, Yunkai Zhang, Binbin Chen +6
Modern industry-scale data centers need to manage a large number of virtual machines (VMs). Due to the continual creation and release of VMs, many small resource fragments are scat…
Few-Shot Test-Time Optimization Without Retraining for Semiconductor Recipe Generation and Beyond
Shangding Gu, Donghao Ying, Ming Jin +4
We introduce Model Feedback Learning (MFL), a novel test-time optimization framework for optimizing inputs to pre-trained AI models or deployed hardware systems without requiring a…
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
Donghao Ying, Yunkai Zhang, Yuhao Ding +2
We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraint…
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
Mengzi Amy Guo, Donghao Ying, Javad Lavaei +1
This work is dedicated to the algorithm design in a competitive framework, with the primary goal of learning a stable equilibrium. We consider the dynamic price competition between…
A Hitting Time Analysis for Stochastic Time-Varying Functions with Applications to Adversarial Attacks on Computation of Markov Decision Processes
Ali Yekkehkhany, Han Feng, Donghao Ying +1
Stochastic time-varying optimization is an integral part of learning in which the shape of the function changes over time in a non-deterministic manner. This paper considers multip…