30 citations · 62 across the 6 of their papers we have counts for
6 papers
Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games
Zuguang Gao, Qianqian Ma, Tamer Başar +1
Learning in stochastic games is arguably the most standard and fundamental setting in multi-agent reinforcement learning (MARL). In this paper, we consider decentralized MARL in st…
Semi-supervised Domain Adaptive Structure Learning
Can Qin, Lichen Wang, Qianqian Ma +3
Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift ac…
Optimal Vaccine Allocation for Pandemic Stabilization
Qianqian Ma, Yang-Yu Liu, Alex Olshevsky
How to strategically allocate the available vaccines is a crucial issue for pandemic control. In this work, we propose a mathematical framework for optimal stabilizing vaccine allo…
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Qianqian Ma, Alex Olshevsky
We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknow…
Optimal Lockdown for Pandemic Control
Qianqian Ma, Yang-Yu Liu, Alex Olshevsky
As a common strategy of contagious disease containment, lockdowns will inevitably weaken the economy. The ongoing COVID-19 pandemic underscores the trade-off arising from public he…
Contradictory Structure Learning for Semi-supervised Domain Adaptation
Can Qin, Lichen Wang, Qianqian Ma +3
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias…