Mohammad Havaei

FL Games: A federated learning framework for distribution shifts

Niladri Chatterjee

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However… (voir plus), participating clients typically each hold data from a different distribution, whereby predictive models with strong in-distribution generalization can fail catastrophically on unseen domains. In this work, we argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL Games, a game-theoretic framework for federated learning for learning causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL Games effectively resolves this challenge and exhibits smooth performance curves. Further, FL Games scales well in the number of clients, requires significantly fewer communication rounds, and is agnostic to device heterogeneity. Through empirical evaluation, we demonstrate that FL Games achieves high out-of-distribution performance on various benchmarks.

2022-10-20

NeurIPS.cc/2022/Workshop/Federated_Learning (présentation orale)

doi.org

openreview.net

Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning

Christian Desrosiers

Samira Ebrahimi Kahou

Batch normalization is a staple of computer vision models, including those employed in few-shot learning. Batch nor-malization layers in con… (voir plus)volutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters

2022-06-18

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

doi.org

A Two-Stream Continual Learning System With Variational Domain-Agnostic Feature Replay

Qicheng Lao

Xiang Jiang

Mohammad Havaei

Yoshua Bengio

Learning in nonstationary environments is one of the biggest challenges in machine learning. Nonstationarity can be caused by either task dr… (voir plus)ift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This article aims to tackle this challenge with a modularized two-stream continual learning (CL) system, where the model is required to learn new tasks from a support stream and adapted to new domains in the query stream while maintaining previously learned knowledge. To deal with both drifts within and across the two streams, we propose a variational domain-agnostic feature replay-based approach that decouples the system into three modules: an inference module that filters the input data from the two streams into domain-agnostic representations, a generative module that facilitates the high-level knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We demonstrate the effectiveness of our proposed approach in addressing the two fundamental scenarios and complex scenarios in two-stream CL.

2021-03-03

IEEE Transactions on Neural Networks and Learning Systems (publié)

doi.org

Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay

Qicheng Lao

Xiang Jiang

Mohammad Havaei

Yoshua Bengio

Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (voir plus)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.

2020-03-09

ArXiv (prépublication)

arxiv.org

InfoMask: Masked Variational Latent Representation to Localize Chest Disease

Saeid Asgari Taghanaki

Mohammad Havaei

Tess Berthier

Francis Dutil

Lisa Di Jorio

Ghassan Hamarneh

Yoshua Bengio

2019-10-10

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation 1

Mohammad Havaei

Nicolas Guizard

Nicolas Chapados

Yoshua Bengio

2018-07-04

Signal Processing and Machine Learning for Biomedical Big Data (publié)

doi.org

Learnable Explicit Density for Continuous Latent Space and Variational Inference

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its correspon… (voir plus)ding posterior. First, we decompose the learning of VAEs into layerwise density estimation, and argue that having a flexible prior is beneficial to both sample generation and inference. Second, we analyze the family of inverse autoregressive flows (inverse AF) and show that with further improvement, inverse AF could be used as universal approximation to any complicated posterior. Our analysis results in a unified approach to parameterizing a VAE, without the need to restrict ourselves to use factorial Gaussians in the latent real space.

2017-10-06

ArXiv (prépublication)

arxiv.org