5.1k citations · 7.5k across the 42 of their papers we have counts for
9 papers · 1 filter
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Michael Kuchnik, Ana Klimovic, Jiri Simsa +2
Input pipelines, which ingest and transform input data, are an essential part of training Machine Learning (ML) models. However, it is challenging to implement efficient input pipe…
On Tilted Losses in Machine Learning: Theory and Applications
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Exponential tilting is a technique commonly used in fields such as statistics, probability, information theory, and optimization to create parametric distribution shifts. Despite i…
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relev…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
On Large-Cohort Training for Federated Learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo +2
Federated learning methods typically learn a model by iteratively sampling updates from a population of clients. In this work, we explore how the number of clients sampled at each…
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li +4
Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are…