110 citations · 250 across the 27 of their papers we have counts for
6 papers · 1 filter
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
Youngjune Lee, Oh Joon Kwon, Haeju Lee +3
Data scarcity and noise are important issues in industrial applications of machine learning. However, it is often challenging to devise a scalable and generalized approach to addre…
Gradient Inversion with Generative Image Prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee +2
Federated Learning (FL) is a distributed learning framework, in which the local data never leaves clients devices to preserve privacy, and the server trains models on the data via…
Sample Selection for Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning…
Improving Fairness via Federated Learning
Yuchen Zeng, Hongxu Chen, Kangwook Lee
Recently, lots of algorithms have been proposed for learning a fair classifier from decentralized data. However, many theoretical and algorithmic questions remain open. First, is f…
Coded-InvNet for Resilient Prediction Serving Systems
Tuan Dinh, Kangwook Lee
Inspired by a new coded computation algorithm for invertible functions, we propose Coded-InvNet a new approach to design resilient prediction serving systems that can gracefully ha…
Permutation-Based SGD: Is Random Optimal?
Shashank Rajput, Kangwook Lee, Dimitris Papailiopoulos
A recent line of ground-breaking results for permutation-based SGD has corroborated a widely observed phenomenon: random permutations offer faster convergence than with-replacement…