Publications

Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study

Jonathan Jalbert

Jean-Noel Weller

Pierre-Luc Boivin

Sylvain Lavigne

Mehdi Taobane

Mike Pieper

Andrea Lodi

Heloise Cardinal

2023-06-02

Canadian Journal of Kidney Health and Disease (published)

Communication Load Balancing via Efficient Inverse Reinforcement Learning

Abhisek Konar

Di Wu

Yi Tian Xu

Seowoo Jang

Steve Liu

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for net… (see more)work systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical RL for load balancing, however, we need an explicit reward definition. Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function. In this work, we tackle the communication load balancing problem from an inverse reinforcement learning (IRL) approach. To the best of our knowledge, this is the first time IRL has been successfully applied in the field of communication load balancing. Specifically, first, we infer a reward function from a set of demonstrations, and then learn a reinforcement learning load balancing policy with the inferred reward function. Compared to classical RL-based solution, the proposed solution can be more general and more suitable for real-world scenarios. Experimental evaluations implemented on different simulated traffic scenarios have shown our method to be effective and better than other baselines by a considerable margin.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (published)

Discussion of “Experimental Study of the Thixotropic Strength Recovery and Microstructural Evolution of Marine Clays”

Xianwei Zhang

Xinyu Liu

Gang Wang

2023-06-01

Journal of Geotechnical and Geoenvironmental Engineering (published)

Discussion of “Experimental Study of the Thixotropic Strength Recovery and Microstructural Evolution of Marine Clays”

Xianwei Zhang

Xinyu Liu

Gang Wang

2023-06-01

Journal of Geotechnical and Geoenvironmental Engineering (published)

Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data.

Guillaume Lam

Irina Rish

P. Dixon

2023-06-01

Journal of Biomechanics (published)

Fast Fine-Tuning Using Curriculum Domain Adaptation

Lulan Shen

Ibtihel Amara

Ruofeng Li

Brett Meyer

Warren Gross

James J. Clark

Current deep neural networks (DNNs) have achieved remarkable accuracy in various downstream tasks. However, their training and fine-tuning a… (see more)re challenging due to several factors, such as limited computational resources, extended training and fine-tuning times, and over-fitting due to small datasets. To address these challenges, we propose a three-stage fast fine-tuning method that efficiently trains DNNs for edge devices. Our method combines curriculum learning and domain adaptation techniques to accelerate training while achieving comparable performance. First, we develop a data curriculum approach, which ranks the dataset according to difficulty and split it into the source domain (containing easy data) and the target domain (containing difficult data). Second, we adapt the pretrained model from the source domain to the target domain using an unsupervised domain adaptation (UDA) method called Deep CORAL. Finally, we continue training the adapted model on the source domain with fewer epochs. Our method achieves high accuracy quickly on various modern neural network architectures and datasets such as CIFAR-10, CIFAR-100, and CINIC-10.

2023-06-01

Canadian Conference on Computer and Robot Vision (published)

Geometry Regularized Autoencoders

Andres F. Duque Correa

Sacha Morin

Guy Wolf

Kevin R. Moon

A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for… (see more) faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning. However, these methods typically only provide an embedding of the input data and cannot extend naturally to new data points. Autoencoders have also become popular for representation learning. While they naturally compute feature extractors that are extendable to new data and invertible (i.e., reconstructing original features from latent representation), they often fail at representing the intrinsic data geometry compared to kernel-based manifold learning. We present a new method for integrating both approaches by incorporating a geometric regularization term in the bottleneck of the autoencoder. This regularization encourages the learned latent representation to follow the intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and preserving invertibility. We compare our approach to autoencoder models for manifold learning to provide qualitative and quantitative evidence of our advantages in preserving intrinsic structure, out of sample extension, and reconstruction. Our method is easily implemented for big-data applications, whereas other methods are limited in this regard.

2023-06-01

IEEE Transactions on Pattern Analysis and Machine Intelligence (published)

Grow-push-prune: Aligning deep discriminants for effective structural network compression

Qing Tian

Tal Arbel

James J. Clark

2023-06-01

Computer Vision and Image Understanding (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

Xue (Steve) Liu

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

Xue (Steve) Liu

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

Xue (Steve) Liu

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)