1.6k citations · 3.6k across the 48 of their papers we have counts for
6 papers · 1 filter
Efficient Methods for Online Multiclass Logistic Regression
Naman Agarwal, Satyen Kale, Julian Zimmert
Multiclass logistic regression is a fundamental task in machine learning with applications in classification and boosting. Previous work (Foster et al., 2018) has highlighted the i…
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…
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs
Satyen Kale, Ayush Sekhari, Karthik Sridharan
Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the method of choice for learning with large over-parameterized models. A popular theory for explaining why SGD…
Federated Functional Gradient Boosting
Zebang Shen, Hamed Hassani, Satyen Kale +1
In this paper, we initiate a study of functional minimization in Federated Learning. First, in the semi-heterogeneous setting, when the marginal distributions of the feature vector…
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Jacob Abernethy, Pranjal Awasthi, Satyen Kale
Alongside the well-publicized accomplishments of deep neural networks there has emerged an apparent bug in their success on tasks such as object recognition: with deep models train…
Learning with User-Level Privacy
Daniel Levy, Ziteng Sun, Kareem Amin +4
We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samp…