Ethan Fetaya

Understanding the Limitations of Conditional Generative Models

2019 · arXiv (Cornell University)

Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate …

Personalized Federated Learning using Hypernetworks

2021 · arXiv (Cornell University)

Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models in a collaborative way while accounting for data disparities …

Multi-Task Learning as a Bargaining Game

2022 · arXiv (Cornell University)

In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks. Joint training reduces computation costs and improves data efficiency; however, since the gradients of these different tasks may conflict, …