6 citations · 16 across the 12 of their papers we have counts for
6 papers · 1 filter
Uncertainty Herding: One Active Learning Method for All Label Budgets
Wonho Bae, Gabriel L. Oliveira, Danica J. Sutherland
Most active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are s…
Generalized Coverage for More Robust Low-Budget Active Learning
Wonho Bae, Junhyug Noh, Danica J. Sutherland
The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to "cover" the data distribution with balls of a given…
AdaFlood: Adaptive Flood Regularization
Wonho Bae, Yi Ren, Mohamad Osama Ahmed +3
Although neural networks are conventionally optimized towards zero training loss, it has been recently learned that targeting a non-zero training loss threshold, referred to as a f…
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Wonho Bae, Jing Wang, Danica J. Sutherland
Most meta-learning methods assume that the (very small) context set used to establish a new task at test time is passively provided. In some settings, however, it is feasible to ac…
How to prepare your task head for finetuning
Yi Ren, Shangmin Guo, Wonho Bae +1
In deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-t…
Meta Temporal Point Processes
Wonho Bae, Mohamed Osama Ahmed, Frederick Tung +1
A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network…