91 citations · 270 across the 62 of their papers we have counts for
4 papers · 1 filter
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann +5
Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets. However, they can be prohibitively expensive to app…
Coresets for Data-efficient Training of Machine Learning Models
Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec
Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained ef…
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger
In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We int…
On the Fairness of Time-Critical Influence Maximization in Social Networks
Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty +3
Influence maximization has found applications in a wide range of real-world problems, for instance, viral marketing of products in an online social network, and information propaga…