Portrait of Xue (Steve) Liu is unavailable

Xue (Steve) Liu

Associate Academic Member
Full Professor, McGill University, School of Computer Science
Vice President Research and Development, Chief Scientist and Co-Director, Samsung's Montreal AI Center
Research Topics
Deep Learning

Biography

Xue (Steve) Liu is an associate academic member of Mila – Quebec Artificial Intelligence Institute and full professor at McGill University’s School of Computer Science.

He is also a William Dawson Scholar at McGill, as well as a professor (courtesy appointment) in the Department of Mathematics and Statistics, associate member of the Centre for Intelligent Machines (CIM), and associate member of the Centre for Advanced Systems and Technologies in Communications (SYTACom).

Liu is VP of R&D, chief scientist and co-director of Samsung AI Center Montréal. Before that, he was chief scientist in charge of research and innovation at Tinder Inc., the world’s largest dating and social discovery app, then valued at over US$10 billion.

He is a Fellow of the IEEE and the Canadian Academy of Engineering in addition to being the recipient of many awards, including the 2017 Mitacs Award for Exceptional Leadership – Professor; Outstanding Young Canadian Computer Science Researcher Prize from the Canadian Association of Computer Science (2014); and McGill’s Tomlinson Scientist Award for “recognition of excellence and scientific leadership.” He founded McGill’s Cyber-Physical Intelligence Lab in 2007 and still serves as its director.

Liu also briefly served as Samuel R. Thompson Chair Associate Professor in the Department of Computer Science and Engineering at the University of Nebraska-Lincoln, and worked at Hewlett-Packard Labs in Palo Alto (California) and IBM’s Thomas J. Watson Research Center (New York)

Current Students

Mark Bai
Master's Research - McGill University
Github
Can (Sam) Chen
PhD - McGill University
Co-supervisor :
Chris Pal
chencan421@gmail.com
Website
Ralph Cui
Postdoctorate
Website
Google Scholar
Lyu Fuyuan
PhD - McGill University
Co-supervisor :
Jin Guo
Website
Github
Google Scholar
Chengming Hu
PhD - McGill University
chengming.hu@mail.mcgill.ca
Yili Jin
PhD - McGill University
Website
Google Scholar
Xuan Li
PhD - McGill University
Zonglun Li
PhD - McGill University
Junliang Luo
PhD - McGill University
Github
Shaoxiang Qin
PhD - McGill University
Alejandro Salinas-Medina
PhD - McGill University
Website
Github
Google Scholar
Rishabh Thaney
Master's Research - McGill University
rishabh.thaney@mail.mcgill.ca
Github
Dheeraj Vattikonda
Master's Research - McGill University
Haolun Wu
PhD - McGill University
Co-supervisor :
Fernando Diaz
Website
Ye Yuan
PhD - McGill University
ye.yuan3@mail.mcgill.ca
Website
Github
Google Scholar
Dun Yuan
Master's Research - McGill University
Gaspard Zhao
Postdoctorate - McGill University
Co-supervisor :
Gregory Dudek
Website
Shuhao Zheng
PhD - McGill University

Publications

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Fuyuan Lyu
Xing Tang
Hong Zhu
Huifeng Guo
Yingxue Zhang
Ruiming Tang
Xue (Steve) Liu
Click-through rate (CTR) prediction model usually consists of three components: embedding table, feature interaction layer, and classifier. … (see more)Learning embedding table plays a fundamental role in CTR prediction from the view of the model performance and memory usage. The embedding table is a two-dimensional tensor, with its axes indicating the number of feature values and the embedding dimension, respectively. To learn an efficient and effective embedding table, recent works either assign various embedding dimensions for feature fields and reduce the number of embeddings respectively or mask the embedding table parameters. However, all these existing works cannot get an optimal embedding table. On the one hand, various embedding dimensions still require a large amount of memory due to the vast number of features in the dataset. On the other hand, decreasing the number of embeddings usually suffers from performance degradation, which is intolerable in CTR prediction. Finally, pruning embedding parameters will lead to a sparse embedding table, which is hard to be deployed. To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models. Specifically, we propose pruning the redundant embeddings regarding corresponding features' importance by learnable pruning thresholds. Furthermore, we consider assigning various embedding dimensions as one single candidate architecture. To efficiently search the optimal embedding dimensions, we design a uniform embedding dimension sampling scheme to equally train all candidate architectures, meaning architecture-related parameters and learnable thresholds are trained simultaneously in one supernet. We then propose an evolution search method based on the supernet to find the optimal embedding dimensions for each field. Experiments on public datasets show that OptEmbed can learn a compact embedding table which can further improve the model performance.
2022-10-17
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (published)
doi.org
arxiv.org
Pandemic policy assessment by artificial intelligence
Sirui Song
Xue (Steve) Liu
Yong Li
Yang Yu
2022-08-16
Scientific Reports (published)
doi.org
Pandemic policy assessment by artificial intelligence
Sirui Song
Xue (Steve) Liu
Yong Li
Yang Yu
2022-08-16
Scientific Reports (published)
doi.org
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu
Bhaskar Mitra
Chen Ma
Fernando Diaz
Xue (Steve) Liu
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or… (see more) groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.
2022-07-07
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu
Bhaskar Mitra
Chen Ma
Fernando Diaz
Xue (Steve) Liu
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or… (see more) groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.
2022-04-29
ArXiv (preprint)
doi.org
arxiv.org
Structure-aware protein self-supervised learning
Can Chen
Jingbo Zhou
Fan Wang
Xue (Steve) Liu
Dejing Dou
Abstract Motivation Protein representation learning methods have shown great potential to many downstream tasks in biological applications. … (see more)A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information. Results In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method. Availability and implementation The Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.
2022-04-06
ArXiv (preprint)
doi.org
arxiv.org
Peer-to-Peer Energy Trading and Energy Conversion in Interconnected Multi-Energy Microgrids Using Multi-Agent Deep Reinforcement Learning
Tianyi Chen
Shengrong Bu
Xue (Steve) Liu
Jikun Kang
F. Richard Yu
Zhu Han
A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and e… (see more)nvironmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralised energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MG’s average hourly operation cost. The impact of carbon tax pricing is also considered.
2022-01-01
IEEE Transactions on Smart Grid (published)
doi.org
A Cost-Efficient Metadata Scheme for High-Performance Deduplication Systems
Yuxuan Mo
Yu Hua
Pengfei Li
Qin Cao
Xue (Steve) Liu
Data deduplication has been widely used in backup systems to eliminate redundant data, which speeds up the backup process and reduces the st… (see more)orage overhead. Deduplication packs multiple chunks into a large, fixed-size container as a storage unit to maintain the locality and achieve efficient compression. We observe that the traditional containers have low filling ratios due to a large amount of metadata generated by small files. Unfilled containers require more space to store a backup, which decreases the storage efficiency and reduces restore performance. In order to address this problem, we propose a Metadata region Adaptive Container Structure, called MACS. MACS maintains a tag to record the length of metadata region in the container. The boundary between meta-data region and data region is dynamically decided to ensure the maximum space efficiency of the containers. Moreover, we propose a container metadata length-based indexing and cache replacement strategy to allow MACS to be practical in data backup systems. We demonstrate the advantages of MACS with three real world backup datasets. MACS achieves over 95% average container filling ratio, which is significantly higher than existing designs. MACS further achieves better restore performance than the traditional container structure. When combined with existing rewriting method, MACS achieves an efficient trade-off between deduplication ratio and restore performance.
2021-12-20
2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) (published)
doi.org
Design and Implementation of Smooth Renewable Power in Cloud Data Centers
Xinxin Liu
Yu Hua
Xue (Steve) Liu
Ling Yang
Yuanyuan Sun
The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts o… (see more)n environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88 percent on average. We have released the source codes for public use.
2021-04-30
IEEE Transactions on Cloud 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… (see more)-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… (see more)-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
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… (see more)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