Publications

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

2023-02-22

Development (publié)

doi.org

Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship

Andres Ferraro

Gustavo Ferreira

Fernando Diaz

Georgina Born

2023-02-21

ArXiv (prépublication)

doi.org

arxiv.org

Steerable Equivariant Representation Learning

Sangnie Bhardwaj

Willie McClinton

Tongzhou Wang

Guillaume Lajoie

Chen Sun

Phillip Isola

Dilip Krishnan

Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval,… (voir plus) and object detection. Data augmentations are a crucial aspect of pre-training robust representations in both supervised and self-supervised settings. Data augmentations explicitly or implicitly promote invariance in the embedding space to the input image transformations. This invariance reduces generalization to those downstream tasks which rely on sensitivity to these particular data augmentations. In this paper, we propose a method of learning representations that are instead equivariant to data augmentations. We achieve this equivariance through the use of steerable representations. Our representations can be manipulated directly in embedding space via learned linear maps. We demonstrate that our resulting steerable and equivariant representations lead to better performance on transfer learning and robustness: e.g. we improve linear probe top-1 accuracy by between 1% to 3% for transfer; and ImageNet-C accuracy by upto 3.4%. We further show that the steerability of our representations provides significant speedup (nearly 50x) for test-time augmentations; by applying a large number of augmentations for out-of-distribution detection, we significantly improve OOD AUC on the ImageNet-C dataset over an invariant representation.

2023-02-21

ArXiv (prépublication)

doi.org

openreview.net

Workflow Discovery from Dialogues in the Low Data Regime

Amine El hattami

Stefania Raimondo

Issam Hadj Laradji

David Vázquez

Pau Rodríguez

Christopher Pal

Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can someti… (voir plus)mes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We introduce a new problem formulation that we call Workflow Discovery (WD) in which we are interested in the situation where a formal workflow may not yet exist. Still, we wish to discover the set of actions that have been taken to resolve a particular problem. We also examine a sequence-to-sequence (Seq2Seq) approach for this novel task. We present experiments where we extract workflows from dialogues in the Action-Based Conversations Dataset (ABCD). Since the ABCD dialogues follow known workflows to guide agents, we can evaluate our ability to extract such workflows using ground truth sequences of actions. We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance. Our conditioning approach also improves zero-shot and few-shot WD performance when transferring learned models to unseen domains within and across datasets. Further, on ABCD a modified variant of our Seq2Seq method achieves state-of-the-art performance on related but different problems of Action State Tracking (AST) and Cascading Dialogue Success (CDS) across many evaluation metrics.

2023-02-19

TMLR (accepté)

doi.org

openreview.net

Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

Rupali Bhati

Jennifer Jones

Audrey Durand

Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (voir plus)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.

2023-02-18

ArXiv (prépublication)

doi.org

arxiv.org

LAGrad: Statically Optimized Differentiable Programming in MLIR

Mai Jacob Peng

Christophe Dubach

2023-02-16

International Conference on Compiler Construction (publié)

doi.org

Spatio-temporal hard attention learning for skeleton-based activity recognition

Bahareh Nikpour