28 citations · 325 across the 73 of their papers we have counts for
10 papers · 1 filter
Cross-Domain Federated Learning in Medical Imaging
Vishwa S Parekh, Shuhao Lai, Vladimir Braverman +4
Federated learning is increasingly being explored in the field of medical imaging to train deep learning models on large scale datasets distributed across different data centers wh…
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
Jingfeng Wu, Difan Zou, Vladimir Braverman +2
Stochastic gradient descent (SGD) has been shown to generalize well in many deep learning applications. In practice, one often runs SGD with a geometrically decaying stepsize, i.e.…
Symmetric Norm Estimation and Regression on Sliding Windows
Vladimir Braverman, Viska Wei, Samson Zhou
The sliding window model generalizes the standard streaming model and often performs better in applications where recent data is more important or more accurate than data that arri…
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
For the problem of task-agnostic reinforcement learning (RL), an agent first collects samples from an unknown environment without the supervision of reward signals, then is reveale…
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Difan Zou, Jingfeng Wu, Vladimir Braverman +3
Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice, which has been hypothesized to play an important role in the generalization of mod…
Coresets for Clustering with Missing Values
Vladimir Braverman, Shaofeng H. -C. Jiang, Robert Krauthgamer +1
We provide the first coreset for clustering points in that have multiple missing values (coordinates). Previous coreset constructions only allow one missing coordina…