Sihui Dai, Saeed Mahloujifar, et al.
ICML 2023
This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted.
Sihui Dai, Saeed Mahloujifar, et al.
ICML 2023
Xiao Jin, Pin-Yu Chen, et al.
NeurIPS 2021
Shuai Xiao, Junchi Yan, et al.
IEEE Access
Jiajin Zhang, Hanqing Chao, et al.
MICCAI 2023