Xue (Steve) Liu

Ralph Cui

Postdoctorat

chengming.hu@mail.mcgill.ca

Google Scholar

Lyu Fuyuan

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Yili Jin

Doctorat - McGill

rishabh.thaney@mail.mcgill.ca

Google Scholar

Xuan Li

Doctorat - McGill

Zonglun Li

Doctorat - McGill

Junliang Luo

Doctorat - McGill

Github

Shaoxiang Qin

Doctorat - McGill

Alejandro Salinas-Medina

Doctorat - McGill

Maîtrise recherche - McGill

Github

Dheeraj Vattikonda

Maîtrise recherche - McGill

Haolun Wu

Doctorat - McGill

Co-superviseur⋅e :

Fernando Diaz

Dun Yuan

Maîtrise recherche - McGill

Ye Yuan

Doctorat - McGill

ye.yuan3@mail.mcgill.ca

Postdoctorat - McGill

Co-superviseur⋅e :

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

Shuhao Zheng

Doctorat - McGill

Billets de blogue

1 juillet 2024

par

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue Liu

Lire l'article

Bidirectional Learning for Offline Model-based Optimization

20 septembre 2023

Apprentissage bidirectionnel pour l’optimisation hors ligne basée sur un modèle

par

Can Chen

Yingxue Zhang

Xue Liu

Mark Coates

Lire l'article

Publications

Health satisfaction outcome from integrated autonomous mobile clinics

Yuzhang Huang

Shaoshan Liu

Zhongying Pan

Carl Wu

Herng-Chia Chiu

Leiyu Shi

2024-10-22

Scientific Reports (publié)

Robust Guided Diffusion for Offline Black-Box Optimization

Can Chen

Christopher Beckham

Zixuan Liu

Chris Pal

Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two … (voir plus)main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.

2024-10-01

ArXiv (prépublication)

A Survey of Diversification Techniques in Search and Recommendation

Haolun Wu

Yansen Zhang

Chen Ma

Fuyuan Lyu

Bowei He

Fernando Diaz

Bhaskar Mitra

Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)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, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.

2024-10-01

IEEE Transactions on Knowledge and Data Engineering (publié)

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval

Haolun Wu

Ofer Meshi

Masrour Zoghi

Fernando Diaz

Craig Boutilier

MARYAM KARIMZADEHGAN

2024-09-25

NeurIPS.cc/2024/Conference (poster)

openreview.net

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks

Ziquan Liu

Yufei Cui

Yan Yan

Yi Xu

Xiangyang Ji

Antoni B. Chan

In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient healt… (voir plus)h and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making. With extensive research focused on enhancing adversarial robustness through various forms of adversarial training (AT), a notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. To address this gap, this study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community. It is first unveiled that existing CP methods do not produce informative prediction sets under the commonly used

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

Think Before You Act: Decision Transformers with Working Memory

Jikun Kang

Romain Laroche

Xingdi Yuan

Adam Trischler

Jie Fu

Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance rel… (voir plus)ies on massive data and computation. 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. Inspired by this, we propose a 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 Atari games and Meta-World object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

Accelerating Digital Twin Calibration with Warm-Start Bayesian Optimization

Abhisek Konar

Amal Feriani

Di Wu

Seowoo Jang

Digital twins are expected to play an important role in the widespread adaptation of AI-based networking solutions in the real world. The ca… (voir plus)libration of these virtual replicas is critical to ensure a trustworthy replication of the real environment. This work focuses on the input parameter calibration of radio access network (RAN) simulators using real network performance metrics as supervision signals. Usually, the RAN digital twin is considered a black-box function and each calibration problem is viewed as a standalone search problem. RAN simulators are slow and non-differentiable, often posing as the bottleneck in the execution time for these search problems. In this work, we aim to accelerate the search process by reducing the number of interactions with the simulator by leveraging RAN interactions from previous problems. We present a sequential Bayesian optimization framework that uses information from the past to warm-start the calibration process. Assuming that the network performance exhibits gradual and periodic changes, the stored information can be reused in future calibrations. We test our method across multiple physical sites over one week and show that using the proposed framework, we can obtain better calibration with a smaller number of interactions with the simulator during the search phase.

2024-06-09

ICC 2024 - IEEE International Conference on Communications (publié)

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

Junliang Luo

Yi Tian Xu

Di Wu

M. Jenkin

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental a… (voir plus)nd regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.

2024-06-09

2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)

Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization

Jimmy Li

Igor Kozlov

Di Wu

The use of learning-based methods for optimizing cellular radio access networks (RAN) has received increasing attention in recent years. Thi… (voir plus)s coincides with a rapid increase in the number of cell sites worldwide, driven largely by dramatic growth in cellular network traffic. Training and maintaining learned models that work well across a large number of cell sites has thus become a pertinent problem. This paper proposes a scalable framework for constructing a reinforcement learning policy bank that can perform RAN optimization across a large number of cell sites with varying traffic patterns. Central to our framework is a novel application of anomaly detection techniques to assess the compatibility between sites (tasks) and the policy bank. This allows our framework to intelligently identify when a policy can be reused for a task, and when a new policy needs to be trained and added to the policy bank. Our results show that our approach to compatibility assessment leads to an efficient use of computational resources, by allowing us to construct a performant policy bank without exhaustively training on all tasks, which makes it applicable under real-world constraints.

2024-06-09

2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)

Optimizing Energy Saving for Wireless Networks Via Offline Decision Transformer

Yi Tian Xu

Di Wu

M. Jenkin

Seowoo Jang

With the global aim of reducing carbon emissions, energy saving for communication systems has gained tremendous attention. Efficient energy-… (voir plus)saving solutions are not only required to accommodate the fast growth in communication demand but solutions are also challenged by the complex nature of the load dynamics. Recent reinforcement learning (RL)-based methods have shown promising performance for network optimization problems, such as base station energy saving. However, a major limitation of these methods is the requirement of online exploration of potential solutions using a high-fidelity simulator or the need to perform exploration in a real-world environment. We circumvent this issue by proposing an offline reinforcement learning energy saving (ORES) framework that allows us to learn an efficient control policy using previously collected data. We first deploy a behavior energy-saving policy on base stations and generate a set of interaction experiences. Then, using a robust deep offline reinforcement learning algorithm, we learn an energy-saving control policy based on the collected experiences. Results from experiments conducted on a diverse collection of communication scenarios with different behavior policies showcase the effectiveness of the proposed energy-saving algorithms.

2024-06-09

ICC 2024 - IEEE International Conference on Communications (publié)

PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi

Pierre-Yves Lajoie

Bobak H. Baghi

Sachini Herath

Francois Hogan

This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinea… (voir plus)r factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.). Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones.

2024-06-09

ICC 2024 - IEEE International Conference on Communications (publié)

Probabilistic Mobility Load Balancing for Multi-band 5G and Beyond Networks

Saria Al Lahham

Di Wu

Ekram Hossain

2024-06-09

2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)