Publications

Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning

Mohammad Karami

Hervé Lombaert

David Rivest‐henault

2023-02-28

Computerized Medical Imaging and Graphics (publié)

doi.org

arxiv.org

Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics

Sagar Sudhakara

Aditya Mahajan

Ashutosh Nayyar

Yi Ouyang

In this article, we consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems … (voir plus)connected over a network. Our goal is to minimize and quantify the regret (i.e., loss in performance) of our learning and control strategy with respect to an oracle who knows the system model. Upfront viewing the interconnected subsystems globally and directly using existing LQG learning algorithms for the global system results in a regret that increases super-linearly with the number of subsystems. Instead, we propose a new Thompson sampling-based learning algorithm which exploits the structure of the underlying network. We show that the expected regret of the proposed algorithm is bounded by

2023-02-28

IEEE Transactions on Control of Network Systems (publié)

doi.org

arxiv.org

Studying the challenges of developing hardware description language programs

Fatemeh Yousefifeshki

Heng Li

Foutse Khomh

2023-02-28

Information and Software Technology (publié)

doi.org

Tackling Climate Change with Machine Learning

David Rolnick

Priya L. Donti

Lynn H. Kaack

Kelly Kochanski

Alexandre Lacoste

Kris Sankaran

Andrew Slavin Ross

Nikola Milojevic-Dupont

Natasha Jaques

Anna Waldman-Brown

Alexandra Luccioni

Tegan Maharaj

Evan D. Sherwin

S. Karthik Mukkavilli

Konrad P. Kording

Carla Gomes

Andrew Y. Ng

Demis Hassabis

John C. Platt

Felix Creutzig … (voir 2 de plus)

Jennifer Chayes

Yoshua Bengio

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we d… (voir plus)escribe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

2023-02-28

ETH Zurich (publié)

doi.org

arxiv.org

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

Rashid Hussain Khokhar

Benjamin C. M. Fung

Farkhund Iqbal

Khalil Al-Hussaeni

Mohammed Hussain

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting… (voir plus) with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.

2023-02-27

ACM Transactions on Knowledge Discovery from Data (publié)

doi.org

A case–control study on predicting population risk of suicide using health administrative data: a research protocol

JianLi Wang

Fatemeh Gholi Zadeh Kharrat

Jean-François Pelletier

Louis Rochette

Eric Pelletier

Pascale Lévesque

Victoria Massamba

Camille Brousseau-Paradis

Mada Mohammed

Geneviève Gariépy

Christian Gagné

Alain Lesage

2023-02-26

BMJ Open (publié)

doi.org

DisKeyword: Tweet Corpora Exploration for Keyword Selection

Sacha Lévy

Reihaneh Rabbany

2023-02-26

Web Search and Data Mining (publié)

doi.org

Preference-Based Offline Evaluation

C. Clarke

Fernando Diaz

Negar Arabzadeh

A core step in production model research and development involves the offline evaluation of a system before production deployment. Tradition… (voir plus)al offline evaluation of search, recommender, and other systems involves gathering item relevance labels from human editors. These labels can then be used to assess system performance using offline evaluation metrics. Unfortunately, this approach does not work when evaluating highly effective ranking systems, such as those emerging from the advances in machine learning. Recent work demonstrates that moving away from pointwise item and metric evaluation can be a more effective approach to the offline evaluation of systems. This tutorial, intended for both researchers and practitioners, reviews early work in preference-based evaluation and covers recent developments in detail.

2023-02-26

Web Search and Data Mining (publié)

doi.org

Lifelong Online Learning from Accumulated Knowledge

Changjian Shui

William Wang

Ihsen Hedhli

Chi Man Wong

Feng Wan

Boyu Wang

Christian Gagné

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task… (voir plus) depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.

2023-02-23

ACM Transactions on Knowledge Discovery from Data (publié)

doi.org

A Learning Metaheuristic Algorithm for a Scheduling Application

Nazgol Niroumandrad

Nadia Lahrichi

Andrea Lodi

2023-02-22

Metaheuristics (publié)

doi.org

New wave theory

Paul François