We study non-modular function maximization in the online interactive ban...
In this manuscript, we offer a gentle review of submodularity and
superm...
Recently a class of generalized information measures was defined on sets...
Refraining from confidently predicting when faced with categories of inp...
With the rapid growth of data, it is becoming increasingly difficult to ...
With increasing data, techniques for finding smaller, yet effective subs...
We study submodular information measures as a rich framework for generic...
Information-theoretic quantities like entropy and mutual information hav...
Submodular Functions are a special class of set functions, which general...
Many machine learning problems reduce to the problem of minimizing an
ex...
Mixup zhang2017mixup is a recently proposed method for training deep
neu...
We introduce a novel method to combat label noise when training deep neu...
We are motivated by large scale submodular optimization problems, where
...
In this paper, we investigate a class of submodular problems which in ge...
We propose a new random pruning method (called "submodular sparsificatio...
Applying submodular maximization in the streaming setting is nontrivial
...
We show that there is a largely unexplored class of functions (positive
...
We reduce a broad class of machine learning problems, usually addressed ...
We study an extension of the classical graph cut problem, wherein we rep...
This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in...