73 citations · 189 across the 16 of their papers we have counts for
5 papers · 1 filter
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Mingrui Zhang, Lin Chen, Hamed Hassani +1
In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from $T^{1/2…
One Sample Stochastic Frank-Wolfe
Mingrui Zhang, Zebang Shen, Aryan Mokhtari +2
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wi…
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
Yumeng Zhang, Gaoguo Jia, Li Chen +2
There is an urgent need for an effective video classification method by means of a small number of samples. The deficiency of samples could be effectively alleviated by generating…
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
Mingrui Zhang, Lin Chen, Aryan Mokhtari +2
How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and h…
Black Box Submodular Maximization: Discrete and Continuous Settings
Lin Chen, Mingrui Zhang, Hamed Hassani +1
In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is…