Xue (Steve) Liu

Ralph Cui

Postdoctorate

Linfeng Du

PhD - McGill University

Lyu Fuyuan

PhD - McGill University

Co-supervisor :

Kaiyuan Hu

PhD - McGill University

Chengming Hu

PhD - McGill University

Li Jiang Jiang

PhD - McGill University

Yili Jin

PhD - McGill University

Hong Kang

PhD - McGill University

Zonglun Li

PhD - McGill University

Yuyan Lin

Master's Research - McGill University

Junliang Luo

PhD - McGill University

Shaoxiang Qin

PhD - McGill University

Alejandro Salinas-Medina

PhD - McGill University

Zipeng Sun

PhD - McGill University

Dheeraj Vattikonda

Master's Research - McGill University

Haolun Wu

PhD - McGill University

Co-supervisor :

Fernando Diaz

Dun Yuan

PhD - McGill University

Ye Yuan

PhD - McGill University

Importance-Aware Co-Teaching for Offline Model-Based Optimization

Weixu Zhang

PhD - McGill University

Blog Posts

Co-enseignement sensible à l’importance pour optimisation hors-ligne fondée sur un modèle

July 1, 2024

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue Liu

Read the article

September 20, 2023

Bidirectional Learning for Offline Model-based Optimization

Can Chen

Yingxue Zhang

Xue Liu

Mark Coates

Read the article

Publications

Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images

Yufei Cui

Ziquan Liu

Xiangyu Liu

Cong Wang

Tei-Wei Kuo

Chun Jason Xue

Antoni Bert Chan

Multiple instance learning (MIL) is a popular weakly-supervised learning model on the whole slide image (WSI) for AI-assisted pathology diag… (see more)nosis. The recent advance in attention-based MIL allows the model to find its region-of-interest (ROI) for interpretation by learning the attention weights for image patches of WSI slides. However, we empirically find that the interpretability of some related methods is either untrustworthy as the principle of MIL is violated or unsatisfactory as the high-attention regions are not consistent with experts’ annotations. In this paper, we propose Bayes-MIL to address the problem from a probabilistic perspective. The induced patch-level uncertainty is proposed as a new measure of MIL interpretability, which outperforms previous methods in matching doctors annotations. We design a slide-dependent patch regularizer (SDPR) for the attention, imposing constraints derived from the MIL assumption, on the attention distribution. SDPR explicitly constrains the model to generate correct attention values. The spatial information is further encoded by an approximate convolutional conditional random field (CRF), for better interpretability. Experimental results show Bayes-MIL outperforms the related methods in patch-level and slide-level metrics and provides much better interpretable ROI on several large-scale WSI datasets.

2023-01-01

International Conference on Learning Representations (published)

dblp.uni-trier.de

A Distributed Pricing Strategy for Edge Computation Offloading Optimization in Autonomous Driving

Jie Tang

Weilin Zhu

Xiaoming Li

Shaoshan Liu

The increase of on-vehicle applications has brought explosive computation demands to autonomous vehicles and overwhelmed their limited onboa… (see more)rd resources. Edge computing can offload application load and effectively alleviate this problem. However, the introduction of edge computing faces significant challenges, including the considerable amount of resource contention due to the scarcity of edge resources and the competition among edge computing resource providers to earn usersâ€™ services requests. We notice that the problem is not purely technical as solutions for these two problems can become conflicting to each other. In this paper, we propose a distributed pricing strategy to achieve full use of computing resources at the edge and maximize the revenue of service operators, both with guaranteed quality-of-service of on-vehicle applications. More specifically, we first use the multi-leader multi-follower Stackelberg game theory to model the pricing of on-vehicle task offloading under edge computing. Next, we propose a distributed pricing strategy to enable edge servers to adjust their local price distributions so that edge servers can bargain with offloading requesters independently. Experimental results confirm that the proposed distributed pricing strategy can provide more optimized server computing resource utilization while guaranteeing the performance of in-vehicle applications.

2023-01-01

IEEE Network (published)

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

Yansen Zhang

Fuyuan Lyu

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

Dynamic Consolidation for Continual Learning

Hang Li

Chen Ma

X. T. 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

Yingxue Zhang

Ruiming Tang

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

Ying Li

Yang Yu

2022-08-16

Scientific Reports (published)

Joint Multisided Exposure Fairness for Recommendation

Bhaskar Mitra

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

Bhaskar Mitra

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)