For many optimization problems in machine learning, finding an optimal
s...
We initiate the study of adversarial attacks on models for binary (i.e. ...
We consider the classic problem of (ϵ,δ)-PAC learning a best
arm where t...
Common methods for interpreting neural models in natural language proces...
Despite the vast success of Deep Neural Networks in numerous application...
In this paper we describe a new algorithm called Fast Adaptive Sequencin...
Providing users with alternatives to choose from is an essential compone...
We address the challenge of designing optimal adversarial noise algorith...
In this paper we study the problem of robust influence maximization in t...
In this paper, we analyze a fast parallel algorithm to efficiently selec...
Algorithms are increasingly common components of high-impact decision-ma...
In this paper we study submodular maximization under a matroid constrain...
In this paper we study the limitations of parallelization in convex
opti...
In this paper we consider parallelization for applications whose objecti...
In this paper we study the adaptivity of submodular maximization. Adapti...
We consider the canonical problem of influence maximization in social
ne...
We consider robust optimization problems, where the goal is to optimize ...
We consider the problem of maximizing a monotone submodular function und...