Xue (Steve) Liu

dheeraj.vattikonda@mila.quebec

Google Scholar

Sam Chen

PhD - McGill University

Co-supervisor :

PhD - McGill University

chengming.hu@mila.quebec

Dheeraj Vattikonda

Master's Research - McGill University

Dun Yuan

Master's Research - McGill University

dun.yuan@mila.quebec

Gaspard Zhao

Postdoctorate - McGill University

Co-supervisor :

Gregory Dudek

hanqing.zhao@mila.quebec

Mark Bai

Master's Research - McGill University

mark.bai@mila.quebec

Haolun Wu

PhD - McGill University

haolun.wu@mila.quebec

Junliang Luo

PhD - McGill University

junliang.luo@mila.quebec

rishabh.thaney@mila.quebec

Lyu Fuyuan

PhD - McGill University

Co-supervisor :

Jin Guo

fuyuan.lyu@mila.quebec

Master's Research - McGill University

shaoxiang.qin@mila.quebec

Shaoxiang Qin

PhD - McGill University

Shuhao Zheng

PhD - McGill University

shuhao.zheng@mila.quebec

Xuan Li

PhD - McGill University

xuan.li@mila.quebec

Ye Yuan

PhD - McGill University

Yili Jin

PhD - McGill University

yili.jin@mila.quebec

Google Scholar

Ralph Cui

Postdoctorate

yufei.cui@mila.quebec

Bidirectional Learning for Offline Model-based Optimization

Google Scholar

Zonglun Li

PhD - McGill University

zonglun.li@mila.quebec

Blog Posts

September 20, 2023

Can Chen

Yingxue Zhang

Xue Liu

Mark Coates

Read the article

Publications

A Survey of Diversification Metrics and Approaches in Retrieval Systems: From the Perspective of Search and Recommendation

Haolun Wu

Yansen Zhang

Chen Ma

Fuyuan Lyu

Fernando Diaz

Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (see more)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems

2023-01-01

(published)

www.semanticscholar.org

Dynamic Consolidation for Continual Learning

Hang Li

Chen Ma

Xi Chen

Abstract Training deep learning models from a stream of nonstationary data is a critical problem to be solved to achieve general artificial … (see more)intelligence. As a promising solution, the continual learning (CL) technique aims to build intelligent systems that have the plasticity to learn from new information without forgetting the previously obtained knowledge. Unfortunately, existing CL methods face two nontrivial limitations. First, when updating a model with new data, existing CL methods usually constrain the model parameters within the vicinity of the parameters optimized for old data, limiting the exploration ability of the model; second, the important strength of each parameter (used to consolidate the previously learned knowledge) is fixed and thus is suboptimal for the dynamic parameter updates. To address these limitations, we first relax the vicinity constraints with a global definition of the important strength, which allows us to explore the full parameter space. Specifically, we define the important strength as the sensitivity of the global loss function to the model parameters. Moreover, we propose adjusting the important strength adaptively to align it with the dynamic parameter updates. Through extensive experiments on popular data sets, we demonstrate that our proposed method outperforms the strong baselines by up to 24% in terms of average accuracy.

2022-12-14

Neural Computation (published)

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Haolun Wu

Chen Ma

Yingxue Zhang

Ruiming Tang

Mark Coates

2022-10-17

Proceedings of the 31st ACM International Conference on Information & Knowledge Management (published)

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

Fuyuan Lyu

Xing Tang

Hong Zhu

Huifeng Guo

Yingxue Zhang

Ruiming Tang

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)

Pandemic policy assessment by artificial intelligence

Sirui Song

Yong Li

Yang Yu

2022-08-16

Scientific Reports (published)

Pandemic policy assessment by artificial intelligence

Sirui Song

Yong Li

Yang Yu

2022-08-16

Scientific Reports (published)

Joint Multisided Exposure Fairness for Recommendation

Haolun Wu

Bhaskar Mitra

Chen Ma

Fernando Diaz

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)

Joint Multisided Exposure Fairness for Recommendation

Haolun Wu

Bhaskar Mitra

Chen Ma

Fernando Diaz

2022-04-29

ArXiv (preprint)

Structure-aware protein self-supervised learning

Can Chen

Jingbo Zhou

Fan Wang

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)

Peer-to-Peer Energy Trading and Energy Conversion in Interconnected Multi-Energy Microgrids Using Multi-Agent Deep Reinforcement Learning

Tianyi Chen

Shengrong Bu

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 MGâ€™s average hourly operation cost. The impact of carbon tax pricing is also considered.

2022-01-01

IEEE Transactions on Smart Grid (published)

A Cost-Efficient Metadata Scheme for High-Performance Deduplication Systems

Yuxuan Mo

Yu Hua

Pengfei Li

Qin Cao

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)

Design and Implementation of Smooth Renewable Power in Cloud Data Centers

Xinxin Liu

Yu Hua

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)