# Journées de l'optimisation 2022

## HEC Montréal, Québec, Canada, 16 — 18 mai 2022

Horaire Auteurs Mon horaire

# WB7 - Optimization and Learning II

### 18 mai 2022 13h30 – 15h10

#### Salle: Quebecor (jaune)

Présidée par Dounia Lakhmiri

## 4 présentations

• 13h30 - 13h55

### Optimization for Federated Learning

• Guojun Zhang, prés., Huawei Noah's Ark Lab

With the wide deployment of machine learning (ML) systems, it has been a critical problem to train ML models while keeping the privacy of user data. Federated learning (FL) is such a scheme that aims to solve the privacy issue. The optimization of FL can be traced back to the established field of distributed optimization, but is more challenging due to the heterogeneity of various clients. In this tutorial, I will introduce federated optimization, including the problem setting, the main challenges as well as different recent approaches to federated optimization. I will conclude this talk by highlighting some unresolved problems that are important for future research.

• 13h55 - 14h20

### The role of AI in enhancing students learning experience based on the Andragogy learning model: The perspectives in Industrial engineering courses

• Ahmad Shahnejat Bushehri, prés., Polytechnique Montreal
• Samira Keivanpour, Polytechnique Montreal

Artificial Intelligence (AI) has changed the paradigm in education and training. The Andragogy model is one of the best learning models in the active pedagogical context. The principles of the model include involving the students in the planning and evaluation phase, learning by doing for an active learning experience, and real-life problem-based learning. We have adopted this model in the context of teaching industrial engineering courses and presented the contribution of AI in facilitating the elements of this model. A brief literature review is provided to show the integration of clustering, data mining, and learning-based algorithms in the educational context. The examples in the production/manufacturing and sustainable manufacturing courses are provided to show the implications on the quality of teaching and students' learning experience.

• 14h20 - 14h45

### Copula transfer learning for population synthesis

• Pascal Jutras-Dubé, prés., University of Montreal
• Fabian Bastin, University of Montreal
• Cinzia Cirillo, University of Maryland
• Javier Bas, University of Maryland
We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient substructure. Our regularizer is only assumed to be lower semicontinuous and prox-bounded. We combine an adaptive quadratic regularization approach with proximal stochastic gradient principles to derive a new solver, called SR2, whose convergence and worst-case complexity do not require knowledge or approximation of the gradient's Lipschitz constant. Our experiments on network instances trained on CIFAR-10 and CIFAR-100 with $\ell_1$ and $\ell_0$ regularization show that SR2 achieves competitive accuracy and sparsity levels.