Portrait of Julien Pallage is unavailable

Julien Pallage

Master's Research - Polytechnique Montréal
Supervisor
Antoine Lesage-Landry
Research Topics
Dynamical Systems
Optimization
GitHub

Publications

Sliced-Wasserstein Distance-based Data Selection
Julien Pallage
Antoine Lesage-Landry
2025-04-17
ArXiv (preprint)
arxiv.org
arxiv.org
Sliced-Wasserstein Distance-based Data Selection
Julien Pallage
Antoine Lesage-Landry
We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learn… (see more)ing approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.
2025-04-01
arXiv (published)
doi.org
arxiv.org
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Julien Pallage
Bertrand Scherrer
Salma Naccache
Christophe B'elanger
Antoine Lesage-Landry
2024-10-29
ArXiv (preprint)
doi.org
arxiv.org
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Julien Pallage
Bertrand Scherrer
Salma Naccache
Christophe B'elanger
Antoine Lesage-Landry
In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering tech… (see more)nique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.
2024-10-29
ArXiv (preprint)
doi.org
arxiv.org
Wasserstein Distributionally Robust Shallow Convex Neural Networks
Julien Pallage
Antoine Lesage-Landry
2024-07-23
ArXiv (preprint)
doi.org
arxiv.org
Wasserstein Distributionally Robust Shallow Convex Neural Networks
Julien Pallage
Antoine Lesage-Landry
In this work, we propose Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear pre… (see more)dictions when subject to adverse and corrupted datasets. Our approach is based on a new convex training program for
2024-07-23
ArXiv (preprint)
doi.org
arxiv.org
Online Dynamic Submodular Optimization
Antoine Lesage-Landry
Julien Pallage
We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. W… (see more)e consider the subset of problems in which the objective function is submodular. We propose the online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hardness of the original problem. We extend OSGA to a generic approximation function. We show that OSGA has a dynamic regret bound similar to the tightest bounds in online convex optimization with respect to the time horizon and the cumulative round optimum variation. For instances where no approximation exists or a computationally simpler implementation is desired, we design the online submodular projected gradient descent (OSPGD) by leveraging the Lova\'sz extension. We obtain a regret bound that is akin to the conventional online gradient descent (OGD). Finally, we numerically test our algorithms in two power system applications: fast-timescale demand response and real-time distribution network reconfiguration.
2023-06-19
ArXiv (preprint)
doi.org
arxiv.org