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Xue (Steve) Liu

Membre académique associé
Professeur titulaire, McGill University, École d'informatique
Vice-président, recherche et développement, directeur scientifique et co-directeur, Samsung's Montreal AI Center

Biographie

Xue (Steve) Liu est professeur titulaire à l'École d'informatique de l’Université McGill, ainsi que vice-président de la recherche et du développement, scientifique en chef et codirecteur du Centre d'IA de Samsung à Montréal. Il est également titulaire d'une bourse William Dawson (professeur titulaire) à l'Université McGill et professeur de mathématiques et de statistiques (nomination de courtoisie) dans le même établissement. Auparavant, il était scientifique en chef chez Tinder Inc., où il dirigeait la recherche et l'innovation touchant l’application de rencontre et de découverte sociale la plus importante au monde, évaluée à plus de 10 milliards de dollars américains.

M. Liu est membre de l'IEEE et membre associé de Mila – Institut québécois d’intelligence artificielle. À l'Université McGill, il est également membre associé du Centre sur les machines intelligentes (CIM) et du Centre sur les systèmes et les technologies avancés en communication (SYTACom). Il a reçu plusieurs récompenses, notamment le prix Mitacs 2017 reconnaissant un leadership exceptionnel parmi le corps professoral, le prix Outstanding Young Canadian Computer Science Researcher de l'Association canadienne de l'informatique en 2014, et le prix Tomlinson Scientist soulignant l'excellence et le leadership scientifique à l'Université McGill. Il est le directeur du Laboratoire sur l’intelligence cyberphysique de l'Université McGill, qu’il a fondé en 2007. Il a également travaillé brièvement en tant que professeur associé de la chaire Samuel R. Thompson au Département d'informatique et d'ingénierie de l'Université du Nebraska à Lincoln, aux laboratoires Hewlett-Packard à Palo Alto, en Californie, et au centre de recherche T. J. Watson d'IBM à New York.

Étudiants actuels

Alejandro Salinas-Medina
Doctorat - McGill University
alejandro.salinas-medina@mila.quebec
Site web
Github
Google Scholar
Sam Chen
Doctorat - McGill University
Co-superviseur⋅e :
Chris Pal
can.chen@mila.quebec
Site web
Chengming Hu
Doctorat - McGill University
chengming.hu@mila.quebec
Dheeraj Vattikonda
Maîtrise recherche - McGill University
dheeraj.vattikonda@mila.quebec
Dun Yuan
Maîtrise recherche - McGill University
dun.yuan@mila.quebec
Gaspard Zhao
Postdoctorat - McGill University
Co-superviseur⋅e :
Gregory Dudek
hanqing.zhao@mila.quebec
Site web
Mark Bai
Maîtrise recherche - McGill University
mark.bai@mila.quebec
Github
Haolun Wu
Doctorat - McGill University
haolun.wu@mila.quebec
Site web
Junliang Luo
Doctorat - McGill University
junliang.luo@mila.quebec
Github
Lyu Fuyuan
Doctorat - McGill University
Co-superviseur⋅e :
Jin Guo
fuyuan.lyu@mila.quebec
Site web
Github
Google Scholar
Rishabh Thaney
Maîtrise recherche - McGill University
rishabh.thaney@mila.quebec
Github
Shaoxiang Qin
Doctorat - McGill University
shaoxiang.qin@mila.quebec
Shuhao Zheng
Doctorat - McGill University
shuhao.zheng@mila.quebec
Xuan Li
Doctorat - McGill University
xuan.li@mila.quebec
Ye Yuan
Doctorat - McGill University
ye.yuan@mila.quebec
Site web
Github
Google Scholar
Yili Jin
Doctorat - McGill University
yili.jin@mila.quebec
Site web
Google Scholar
Ralph Cui
Postdoctorat
yufei.cui@mila.quebec
Site web
Google Scholar
Zonglun Li
Doctorat - McGill University
zonglun.li@mila.quebec

Publications

Smart Futures Based Resource Trading and Coalition Formation for Real-Time Mobile Data Processing
Ruitao Chen
Xianbin Wang
Xue (Steve) Liu
Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer… (voir plus)-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.
2021-02-18
IEEE Transactions on Services Computing (published)
doi.org
Smart Futures Based Resource Trading and Coalition Formation for Real-Time Mobile Data Processing
Ruitao Chen
Xianbin Wang
Xue (Steve) Liu
Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer… (voir plus)-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.
2021-02-18
IEEE Transactions on Services Computing (publié)
doi.org
Variational Nested Dropout
Yufei Cui
Yushun Mao
Ziquan Liu
Qiao Li
Antoni Bert Chan
Xue (Steve) Liu
Tei-Wei Kuo
Chun Jason Xue
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance du… (voir plus)ring training. It has been explored for: I. Constructing nested nets Cui et al. 2020, Cui et al. 2021: the nested nets are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. The nested dropout implicitly ranks the network parameters, generating a set of sub-networks such that any smaller sub-network forms the basis of a larger one. II. Learning ordered representation Rippel et al. 2014: the nested dropout applied to the latent representation of a generative model (e.g., auto-encoder) ranks the features, enforcing explicit order of the dense representation over dimensions. However, the dropout rate is fixed as a hyper-parameter during the whole training process. For nested nets, when network parameters are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. For generative models, the importance of features is specified as a constant vector, restraining the flexibility of representation learning. To address the problem, we focus on the probabilistic counterpart of the nested dropout. We propose a variational nested dropout (VND) operation that draws samples of multi-dimensional ordered masks at a low cost, providing useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the parameter distributions. We further exploit the VND under different generative models for learning ordered latent distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related generative models on data generation tasks.
2021-01-27
ArXiv (preprint)
doi.org
arxiv.org