Xue (Steve) Liu

Can (Sam) Chen

PhD - McGill University

Co-supervisor :

Chris Pal

chencan421@gmail.com

Ralph Cui

Postdoctorate

chengming.hu@mail.mcgill.ca

Google Scholar

Lyu Fuyuan

PhD - McGill University

Co-supervisor :

PhD - McGill University

Yili Jin

PhD - McGill University

Google Scholar

Xuan Li

PhD - McGill University

Zonglun Li

PhD - McGill University

Junliang Luo

PhD - McGill University

Shaoxiang Qin

PhD - McGill University

Alejandro Salinas-Medina

PhD - McGill University

Master's Research - McGill University

Dheeraj Vattikonda

Master's Research - McGill University

Haolun Wu

PhD - McGill University

Co-supervisor :

Fernando Diaz

Ye Yuan

PhD - McGill University

ye.yuan3@mail.mcgill.ca

Google Scholar

Dun Yuan

Master's Research - McGill University

Gaspard Zhao

Postdoctorate - McGill University

Co-supervisor :

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

Shuhao Zheng

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

ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

Dmitriy Rivkin

Nikhil Kakodkar

David Meger

Oliver Limoyo

Francois Hogan

Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photograp… (see more)hic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.

2023-06-02

2023 IEEE International Conference on Robotics and Automation (ICRA) (published)

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks

Dun Yuan

Yujin Nam

Amal Feriani

Abhisek Konar

Di Wu

Seowoo Jang

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G sys… (see more)tems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (published)

Multi-Agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks

Jikun Kang

Di Wu

Ju Wang

Ekram Hossain

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the… (see more) BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (published)

Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios

Yi Tian Xu

Jimmy Li

Di Wu

M. Jenkin

Seowoo Jang

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving incr… (see more)easing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (published)

Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks

Igor Kozlov

Dmitriy Rivkin

Wei-Di Chang

Di Wu

Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thou… (see more)sands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (published)

Reinforcement learning for communication load balancing: approaches and challenges

Di Wu

Jimmy Li

Amal Ferini

Yi Tian Xu

M. Jenkin

Seowoo Jang

The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for e… (see more)nhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

2023-05-31

Frontiers of Computer Science (published)

Think Before You Act: Decision Transformers with Internal Working Memory

Jikun Kang

Romain Laroche

Xingdi Yuan

Adam Trischler

Jie Fu

Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performan… (see more)ce relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model's performance on previous tasks. In contrast to LLMs' implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Thus inspired, we propose an internal working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in both Atari games and meta-world object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.

2023-05-24

ArXiv (preprint)

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Ju Wang

Xi Chen

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond… (see more) (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

2023-05-16

ACM Transactions on Sensor Networks (published)

Embracing Channel Estimation in Multi-Packet Reception of ZigBee

Zhe Wang

Linghe Kong

Guihai Chen

As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Sinc… (see more)e ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of

2023-05-01

IEEE Transactions on Mobile Computing (published)

Structure-aware protein self-supervised learning

Can Chen

Jingbo Zhou

Fan Wang

Dejing Dou

2023-04-13

Bioinformatics (published)

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

Haolun Wu

Yingxue Zhang

Chen Ma

Wei Guo

Ruiming Tang

Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and i… (see more)tem representations. Many state-of-the-art (SOTA) methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks (GNNs) to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information (tags) associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts which is the correct pairing between the representations obtained from the users that have interacted with this item and the tags assigned to it. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the users’ decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose a user intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.

2023-04-03

2023 IEEE 39th International Conference on Data Engineering (ICDE) (published)

Ternary Quantization: A Survey

Danyang Liu

Inference time, model size, and accuracy are critical for deploying deep neural network models. Numerous research efforts have been made to … (see more)compress neural network models with faster inference and higher accuracy. Pruning and quantization are mainstream methods to this end. During model quantization, converting individual float values of layer weights to low-precision ones can substantially reduce the computational overhead and improve the inference speed. Many quantization methods have been studied, for example, vector quantization, low-bit quantization, and binary/ternary quantization. This survey focuses on ternary quantization. We review the evolution of ternary quantization and investigate the relationships among existing ternary quantization methods from the perspective of projection function and optimization methods.

2023-03-02

ArXiv (preprint)