Xue (Steve) Liu

Jiaxuan Chen

PhD - McGill University

Principal supervisor :

Oana Balmau

Ralph Cui

Postdoctorate

Linfeng Du

PhD - McGill University

Lyu Fuyuan

PhD - McGill University

Co-supervisor :

PhD - McGill University

Kaiyuan 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

Haolun Wu

PhD - McGill University

Co-supervisor :

Fernando Diaz

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

AdaTeacher: Adaptive Multi-Teacher Weighting for Communication Load Forecasting

Chengming Hu

Ju Wang

Di Wu

Yan Xin

Charlie Zhang

To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load… (see more). Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.

2023-12-04

Global Communications Conference (published)

Energy Saving in Cellular Wireless Networks via Transfer Deep Reinforcement Learning

Di Wu

Yi Tian Xu

M. Jenkin

Seowoo Jang

Ekram Hossain

With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been… (see more) increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.

2023-12-04

Global Communications Conference (published)

Learning to Adapt: Communication Load Balancing via Adaptive Deep Reinforcement Learning

Di Wu

Yi Tian Xu

Jimmy Li

M. Jenkin

Ekram Hossain

Seowoo Jang

Yan Xin

Charlie Zhang

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critic… (see more)al to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.

2023-12-04

GLOBECOM 2023 - 2023 IEEE Global Communications Conference (published)

Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing

Ekram Hossain

3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersiv… (see more)e and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.

2023-10-06

ArXiv (preprint)

arxiv.org

A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms

Hanqing Zhao

Alexandre Pacheco

Volker Strobel

Andreagiovanni Reina

Marco Dorigo

Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robo… (see more)tics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.

2023-10-01

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning

Hanqing Zhao

We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in… (see more) advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i.e. fault detection). Current IRL methods for fault detection, however, either use a linear reward representation or require extensive sampling from the environment to estimate the policy, rendering them inappropriate for safety-critical situations where sampling of failure observations via fault injection can be expensive and dangerous. To address this issue, this paper proposes a zero-shot and exogenous fault detector based on an approximate variational reward imitation learning (AVRIL) structure. The fault detector recovers a reward signal as a function of externally observable information to describe the normal operation, which can then be used to detect anomalies caused by faults. Our method incorporates expert knowledge through a customizable reward prior distribution, allowing the fault detector to learn the reward solely from normal operation samples, without the need for a simulator or costly interactions with the environment. We evaluate our approach for exogenous partial fault detection in multi-stage robotic manipulator tasks, comparing it with several baseline methods. The results demonstrate that our method more effectively identifies unseen faults even when they occur within just three controller time steps.

2023-10-01

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

Importance-aware Co-teaching for Offline Model-based Optimization

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Importance-aware Co-teaching for Offline Model-based Optimization

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with application… (see more)s in robot, protein, and molecule design, among others. A prevalent approach is gradient ascent, where a proxy model is trained on the offline dataset and then used to optimize the design. This method suffers from an out-of-distribution issue, where the proxy is not accurate for unseen designs. To mitigate this issue, we explore using a pseudo-labeler to generate valuable data for fine-tuning the proxy. Specifically, we propose

2023-09-21

NeurIPS.cc/2023/Conference (poster)

Parallel-mentoring for Offline Model-based Optimization

Can Chen

Zixuan Liu

Parallel-mentoring for Offline Model-based Optimization

Can Chen

Christopher Beckham

Zixuan Liu

Chris Pal

We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores. These desi… (see more)gns encompass a variety of domains, including materials, robots, DNA sequences, and proteins. A common approach trains a proxy on the static dataset and performs gradient ascent to obtain new designs. However, this often results in poor designs due to the proxy inaccuracies for out-of-distribution designs. Recent studies indicate that (a) gradient ascent with a mean ensemble of proxies generally outperforms simple gradient ascent, and (b) a trained proxy provides weak ranking supervision signals for design selection. Motivated by (a) and (b), we propose

2023-09-21

NeurIPS.cc/2023/Conference (poster)

Retrieval-Augmented Multiple Instance Learning

Yufei Cui

Ziquan Liu

Yixin CHEN

Yuchen Lu

Xinyue Yu

Tei-Wei Kuo

Miguel R. D. Rodrigues

Chun Jason Xue

Antoni B. Chan

Multiple Instance Learning (MIL) is a crucial weakly supervised learning method applied across various domains, e.g., medical diagnosis base… (see more)d on whole slide images (WSIs). Recent advancements in MIL algorithms have yielded exceptional performance when the training and test data originate from the same domain, such as WSIs obtained from the same hospital. However, this paper reveals a performance deterioration of MIL models when tested on an out-of-domain test set, exemplified by WSIs sourced from a novel hospital. To address this challenge, this paper introduces the Retrieval-AugMented MIL (RAM-MIL) framework, which integrates Optimal Transport (OT) as the distance metric for nearest neighbor retrieval. The development of RAM-MIL is driven by two key insights. First, a theoretical discovery indicates that reducing the input's intrinsic dimension can minimize the approximation error in attention-based MIL. Second, previous studies highlight a link between input intrinsic dimension and the feature merging process with the retrieved data. Empirical evaluations conducted on WSI classification demonstrate that the proposed RAM-MIL framework achieves state-of-the-art performance in both in-domain scenarios, where the training and retrieval data are in the same domain, and more crucially, in out-of-domain scenarios, where the (unlabeled) retrieval data originates from a different domain. Furthermore, the use of the transportation matrix derived from OT renders the retrieval results interpretable at the instance level, in contrast to the vanilla

Retrieval-Augmented Multiple Instance Learning

Yufei Cui

Ziquan Liu

Yixin CHEN

Yuchen Lu

Xinyue Yu

Tei-Wei Kuo

Miguel R. D. Rodrigues

Chun Jason Xue

Antoni B. Chan

2023-09-21

NeurIPS.cc/2023/Conference (poster)