Xue (Steve) Liu

Ralph Cui

Postdoctorate

chengming.hu@mail.mcgill.ca

Lyu Fuyuan

PhD - McGill University

Co-supervisor :

PhD - McGill University

Yili Jin

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

Master's Research - McGill University

Haolun Wu

PhD - McGill University

Co-supervisor :

Fernando Diaz

Ye Yuan

PhD - McGill University

ye.yuan3@mail.mcgill.ca

Github

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

Weixu Zhang

PhD - McGill University

Github

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

AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments

Tianyi Yang

Tianze Yang

Andrew Liu

Jie Tang

Na An

Shaoshan Liu

2024-02-06

Connection Science (published)

Real-time monitoring for the next core-collapse supernova in JUNO

Angel Abusleme

Thomas Adam

Shakeel Ahmad

Rizwan Ahmed

Sebastiano Aiello

Muhammad Akram

Abid Aleem

Fengpeng An

Qi An

Giuseppe Andronico

Nikolay Anfimov

Vito Antonelli

Tatiana Antoshkina

Burin Asavapibhop

João Pedro Athayde Marcondes de André

Didier Auguste

Weidong Bai

Nikita Balashov

Wander Baldini

Andrea Barresi … (see 480 more)

Davide Basilico

Eric Baussan

Marco Bellato

Marco Beretta

Antonio Bergnoli

Daniel Bick

Lukas Bieger

Svetlana Biktemerova

Thilo Birkenfeld

Iwan Morton-blake

David Blum

Simon Blyth

Anastasia Bolshakova

Mathieu Bongrand

Clément Bordereau

Dominique Breton

Augusto Brigatti

Riccardo Brugnera

Riccardo Bruno

Antonio Budano

Jose Busto

Anatael Cabrera

Barbara Caccianiga

Hao Cai

Xiao Cai

Yanke Cai

Z. Cai

Stéphane Callier

Antonio Cammi

Agustin Campeny

C. Cao

Guofu Cao

Jun Cao

Rossella Caruso

C. Cerna

Vanessa Cerrone

Chi Chan

Jinfan Chang

Yun Chang

Auttakit Chatrabhuti

Chao Chen

Guoming Chen

Pingping Chen

Shaomin Chen

Yixue Chen

Yu Chen

Zhangming Chen

Zhiyuan Chen

Zikang Chen

Jie Cheng

Yaping Cheng

Yuanyuan Zhang

Alexander Chepurnov

Alexey Chetverikov

Davide Chiesa

Pietro Chimenti

Yen-Ting Chin

Ziliang Chu

Artem Chukanov

Gérard Claverie

Catia Clementi

Barbara Clerbaux

Marta Colomer Molla

Selma Conforti Di Lorenzo

Alberto Coppi

Daniele Corti

Simon Csakli

Flavio Dal Corso

Olivia Dalager

Jaydeep Datta

C. Taille

Zhi Deng

Ziyan Deng

Xiaoyu Ding

Xuefeng Ding

Yayun Ding

Bayu Dirgantara

Carsten Dittrich

Sergey Dmitrievsky

Tadeas Dohnal

Dmitry Dolzhikov

Georgy Donchenko

Jianmeng Dong

Evgeny Doroshkevich

Wei Dou

Marcos Dracos

Frédéric Druillole

Ran Du

S. X. Du

K. Dugas

Stefano Dusini

Hongyue Duyang

J. Eck

Timo Enqvist

Andrea Fabbri

Ulrike Fahrendholz

Lei Fan

Jian Fang

Wen Fang

Marco Fargetta

Dmitry Fedoseev

Zhengyong Fei

Li-Cheng Feng

Qichun Feng

Federico Ferraro

Amélie Fournier

H. Gan

Feng Gao

Alberto Garfagnini

Arsenii Gavrikov

Marco Giammarchi

Nunzio Giudice

Maxim Gonchar

G. Gong

Hui Gong

Yuri Gornushkin

A. Gottel

Marco Grassi

Maxim Gromov

Vasily Gromov

Minghao Gu

X. Gu

Yunting Gu

M. Guan

Yuduo Guan

Nunzio Guardone

Cong Guo

Wanlei Guo

Xinheng Guo

Caren Hagner

Ran Han

Yang Han

Miao He

W. He

Tobias Heinz

Patrick Hellmuth

Yue-kun Heng

Rafael Herrera

Y. Hor

Shaojing Hou

Yee Hsiung

Bei-Zhen Hu

Hang Hu

Jianrun Hu

Jun Hu

Shouyang Hu

Tao Hu

Yuxiang Hu

Zhuojun Hu

Guihong Huang

Hanxiong Huang

Jinhao Huang

Jun-Hao Huang

Kaixuan Huang

Wenhao Huang

Xinting Huang

X. T. Huang

Yongbo Huang

Jiaqi Hui

Lei Huo

Wenju Huo

Cédric Huss

Safeer Hussain

Leonard Imbert

Ara Ioannisian

Roberto Isocrate

Arshak Jafar

Beatrice Jelmini

Ignacio Jeria

Xiaolu Ji

Huihui Jia

Junji Jia

Siyu Jian

Cailian Jiang

Di Jiang

Wei Jiang

Xiaoshan Jiang

X. Jing

Cécile Jollet

Philipp Kampmann

Li Kang

Rebin Karaparambil

Narine Kazarian

Ali Khan

Amina Khatun

Khanchai Khosonthongkee

Denis Korablev

K. Kouzakov

Alexey Krasnoperov

Sergey Kuleshov

Nikolay Kutovskiy

Loïc Labit

Tobias Lachenmaier

Cecilia Landini

Sébastien Leblanc

Victor Lebrin

Frederic Lefevre

Rui Li

Rupert Leitner

Jason Leung

Demin Li

Fei Li

Fule Li

Gaosong Li

Huiling Li

Jiajun Li

Mengzhao Li

Min Li

Nan Li

Qingjiang Li

Ruhui Li

Ruiting Lei

Shanfeng Li

Tao Li

Teng Li

Weidong Li

Weiguo Li

Xiaomei Li

Xiaonan Li

Xinglong Li

Yi Li

Yichen Li

Yufeng Li

Zhaohan Li

Zhibing Li

Ziyuan Li

Zonghui Li

Hao Liang

Jiaming Yan

Ayut Limphirat

G. Lin

Shengxin Lin

Tao Lin

Jiajie Ling

Xin Ling

Ivano Lippi

Caimei Liu

Yang Liu

Fengcheng Liu

Haidong Liu

Hao Liu

Hongbang Liu

Hongjuan Liu

Hongtao Liu

Hui Liu

Jianglai Liu

Jia-xing Liu

Jinchang Liu

Min Liu

Qian Liu

Q. Liu

Runxuan Liu

Sheng Liu

Shubin Liu

Shulin Liu

Xiaowei Liu

Xiwen Liu

Yankai Liu

Zhen Liu

Alexey Lokhov

Paolo Lombardi

Claudio Lombardo

Kai Loo

Chuan Lu

Haoqi Lu

Jingbin Lu

Junguang Lu

Peizhi Lu

Shuxiang Lu

Xianguo Lu

Bayarto Lubsandorzhiev

Sultim Lubsandorzhiev

Livia Ludhova

Arslan Lukanov

Daibin Luo

F. Luo

Guang Luo

Jianyi Luo

Shu Luo

Wuming Luo

Xiaojie Luo

Vladimir Lyashuk

B. Ma

Bing Ma

R. Q. Ma

Si Ma

Xiaoyan Ma

Xubo Ma

Jihane Maalmi

Marco Magoni

Jingyu Mai

Yury Malyshkin

Roberto Carlos Mandujano

Fabio Mantovani

Xin Mao

Yajun Mao

S. Mari

F. Marini

Agnese Martini

Matthias Mayer

Davit Mayilyan

Ints Mednieks

Yu Meng

Anita Meraviglia

Anselmo Meregaglia

Emanuela Meroni

David J. Meyhofer

Lino Miramonti

Nikhil Mohan

Michele Montuschi

Axel Muller

M. Nastasi

Dmitry V. Naumov

Elena Naumova

Diana Navas-Nicolas

Igor Nemchenok

Minh Thuan Nguyen Thi

Alexey Nikolaev

F. Ning

Zhe Ning

Hiroshi Nunokawa

Lothar Oberauer

Juan Pedro Ochoa-Ricoux

Alexander Olshevskiy

Domizia Orestano

Fausto Ortica

Rainer Othegraven

A. Paoloni

Sergio Parmeggiano

Y. P. Pei

Luca Pelicci

Anguo Peng

Yu Peng

Yuefeng Peng

Z-R Peng

Frédéric Perrot

P. Petitjean

Fabrizio Petrucci

Oliver Pilarczyk

Luis Felipe Piñeres Rico

Artyom Popov

Pascal Poussot

Ezio Previtali

Fazhi Qi

M. Qi

Xiaohui Qi

Sen Qian

X. Qian

Zhen Qian

Hao-xue Qiao

Zhonghua Qin

S. Qiu

Manhao Qu

Z. Qu

Gioacchino Ranucci

Reem Rasheed

A. Re

Abdel Rebii

Mariia Redchuk

Bin Ren

Jie Ren

Barbara Ricci

Komkrit Rientong

Mariam Rifai

Mathieu Roche

Narongkiat Rodphai

Aldo M. Romani

Bedřich Roskovec

X. Ruan

Arseniy Rybnikov

Andrey Sadovsky

Paolo Saggese

Deshan Sandanayake

Anut Sangka

G. Sava

Utane Sawangwit

Michaela Schever

Cédric Schwab

Konstantin Schweizer

Alexandr Selyunin

Andrea Serafini

M. Settimo

V. Sharov

Arina Shaydurova

Jingyan Shi

Yanan Shi

Vitaly Shutov

Andrey Sidorenkov

Fedor Šimkovic

Apeksha Singhal

Chiara Sirignano

Jaruchit Siripak

Monica Sisti

Mikhail Smirnov

Oleg Smirnov

Thiago Sogo-Bezerra

Sergey Sokolov

Julanan Songwadhana

Boonrucksar Soonthornthum

Albert Sotnikov

Ondvrej vSr'amek

Warintorn Sreethawong

Achim Stahl

Luca Stanco

Konstantin Stankevich

Hans Steiger

Jochen Steinmann

Tobias Sterr

M. Stock

Virginia Strati

Alexander Studenikin

Aoqi Su

Jun Su

Shifeng Sun

Xilei Sun

Yongjie Sun Sun

Yongzhao Sun

Zhengyang Sun

Narumon Suwonjandee

Michal Szelezniak

Akira Takenaka

Jian Tang

Qiang Tang

Quan Tang

Xiao Tang

Vidhya Thara Hariharan

Eric Theisen

Alexander Tietzsch

Igor Tkachev

Tomas Tmej

M. Torri

Francesco Tortorici

K. Treskov

Andrea Triossi

Riccardo Triozzi

Wladyslaw Trzaska

Y. Tung

Cristina Tuve

Nikita Ushakov

Vadim Vedin

Carlo Venettacci

Giuseppe Verde

Maxim Vialkov

Benoit Viaud

Cornelius Moritz Vollbrecht

Katharina von Sturm

Vit Vorobel

Dmitriy Voronin

Lucia Votano

Pablo Walker

Caishen Wang

Chung-Hsiang Wang

En Wang

Guoli Wang

Jian Wang

Jun Wang

Li Wang

Lucinda W. Wang

Meng Wang

Ruiguang Wang

Siguang Wang

W. Wang

Wenshuai Wang

Xi Wang

Xiangyue Wang

Yangfu Wang

Yaoguang Wang

Yi Xing Wang

Yifang Wang

Yuanqing Wang

Yuyi Wang

Zhe Wang

Zheng Wang

Zhimin Wang

Apimook Watcharangkool

Wei Wei

Wenlu Wei

Yadong Wei

Yuehuan Wei

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detect… (see more)ion of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN.

2024-01-25

Journal of Cosmology and Astroparticle Physics (published)

AIoT Smart Home via Autonomous LLM Agents

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Abhisek Konar

Adam Sigal

2024-01-01

IEEE Internet of Things Journal (published)

Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation

Chengming Hu

Haolun Wu

Xuan Li

Chen Ma

Xi Chen

Jun Yan

Boyu Wang

2023-12-22

ArXiv (preprint)

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

Dun Yuan

Ekram Hossain

Di Wu

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)

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

openreview.net

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

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger