We investigate two new optimization problems—minimizing a submodular function subject
to a submodular lower bound constraint (submodular cover) and maximizing a submodular …

This paper proposes a novel task allocation framework, CrowdTasker, for mobile
crowdsensing. CrowdTasker operates on top of energy-efficient Piggyback Crowdsensing …

We present a practical and powerful new framework for both unconstrained and constrained
submodular function optimization based on discrete semidifferentials (sub-and super …

We investigate three related and important problems connected to machine learning,
namely approximating a submodular function everywhere, learning a submodular function …

We introduce a new multi-stage algorithmic framework for submodular maximization. We are
motivated by extremely large scale machine learning problems, where both storing the …

We analyze the performance of the greedy algorithm, and also a discrete semi-gradient
based algorithm, for maximizing the sum of a suBmodular and suPermodular (BP) function …

We propose a new framework for imitation learning--treating imitation as a two-player
ranking-based game between a policy and a reward. In this game, the reward agent learns …

In classic Reinforcement Learning (RL), the agent maximizes an additive objective of the
visited states, eg, a value function. Unfortunately, objectives of this type cannot model many …

The purpose of this paper is to introduce and analyze two inertial algorithms with self-
adaptive stepsizes for solving variational inequalities in reflexive Banach spaces. Our …

We investigate a new optimization problem involving minimizing the Ratio of Submodular
(RS) functions. We argue that this problem occurs naturally in several real world …