166 citations · 1.4k across the 47 of their papers we have counts for
6 papers · 1 filter
apricot: Submodular selection for data summarization in Python
Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble
We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greed…
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…
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes +2
Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pair…
Combating Label Noise in Deep Learning Using Abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes +2
We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby…
A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems
Rishabh Iyer, Jeff Bilmes
We are motivated by large scale submodular optimization problems, where standard algorithms that treat the submodular functions in the \emph{value oracle model} do not scale. In th…
Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs
Rishabh Iyer, Jeff Bilmes
In this paper, we investigate a class of submodular problems which in general are very hard. These include minimizing a submodular cost function under combinatorial constraints, wh…