28 citations · 325 across the 73 of their papers we have counts for
29 papers · 1 filter
Heavy-Tailed Flow Matching via Random Clocks
Zhouhao Yang, Yezhen Wang, Kenji Kawaguchi +2
Heavy-tailed data arise in many domains where rare events carry disproportionate importance, such as imbalanced image datasets, financial returns, and weather extremes. Standard di…
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environmen…
Multi-environment lifelong deep reinforcement learning for medical imaging
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging. However, the environments for medical imaging tasks are constantly evolving in terms of imaging…
AutoCoreset: An Automatic Practical Coreset Construction Framework
Alaa Maalouf, Murad Tukan, Vladimir Braverman +1
A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries. Coresets became prevalent in machine learni…
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
Recent research has observed that in machine learning optimization, gradient descent (GD) often operates at the edge of stability (EoS) [Cohen, et al., 2021], where the stepsizes a…
Provable Data Subset Selection For Efficient Neural Network Training
Murad Tukan, Samson Zhou, Alaa Maalouf +3
Radial basis function neural networks (\emph{RBFNN}) are {well-known} for their capability to approximate any continuous function on a closed bounded set with arbitrary precision g…