3.3k citations · 3.9k across the 24 of their papers we have counts for
10 papers · 1 filter
Source-free Domain Adaptation Requires Penalized Diversity
Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak +4
While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance of each model is limited to its own dat…
FL Games: A Federated Learning Framework for Distribution Shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
FL Games: A federated learning framework for distribution shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
Farshid Varno, Marzie Saghayi, Laya Rafiee Sevyeri +3
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communic…
Hypothesis Disparity Regularized Mutual Information Maximization
Qicheng Lao, Xiang Jiang, Mohammad Havaei
We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis…
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin +1
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution s…
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb +3
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks. There is a growing…
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Qicheng Lao, Xiang Jiang, Mohammad Havaei +1
Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task drift, i.e., the drift in the conditiona…
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand +5
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by…
Learnable Explicit Density for Continuous Latent Space and Variational Inference
Chin-Wei Huang, Ahmed Touati, Laurent Dinh +4
In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior. First, we decompose the…