This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …

A number of online services nowadays rely upon machine learning to extract valuable
information from data collected in the wild. This exposes learning algorithms to the threat of …

We investigate a problem at the intersection of machine learning and security: training-set
attacks on machine learners. In such attacks an attacker contaminates the training data so …

I draw the reader's attention to machine teaching, the problem of finding an optimal training
set given a machine learning algorithm and a target model. In addition to generating …

In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …

A good teacher can adjust the curriculum based on students' learning history. By analogy, in
this paper, we study the dynamics of a deep neural network's (DNN) performance on …

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a
robot's behavior in novel situations. And since a robot's behavior is often a direct result of its …

Higher-level cognition depends on the ability to learn models of the world. We can
characterize this at the computational level as a structure-learning problem with the goal of …

How to propagate label information from labeled examples to unlabeled examples over a
graph has been intensively studied for a long time. Existing graph-based propagation …