4 citations · 4 across the 2 of their papers we have counts for
6 papers
Test-time Batch Normalization
Tao Yang, Shenglong Zhou, Yuwang Wang +2
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of…
Sparse SVM for Sufficient Data Reduction
Shenglong Zhou
Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in various applications. However, they may incur prohibitive computational costs f…
Theoretical and numerical comparison of the Karush-Kuhn-Tucker and value function reformulations in bilevel optimization
Alain Zemkoho, Shenglong Zhou
The Karush-Kuhn-Tucker and value function (lower-level value function, to be precise) reformulations are the most common single-level transformations of the bilevel optimization pr…
Semismooth Newton-type method for bilevel optimization: Global convergence and extensive numerical experiments
Andreas Fischer, Alain B. Zemkoho, Shenglong Zhou
We consider the standard optimistic bilevel optimization problem, in particular upper- and lower-level constraints can be coupled. By means of the lower-level value function, the p…
Support Vector Machine Classifier via Soft-Margin Loss
Huajun Wang, Yuanhai Shao, Shenglong Zhou +2
Support vector machine (SVM) has attracted great attentions for the last two decades due to its extensive applications, and thus numerous optimization models have been proposed. To…
BOLIB: Bilevel Optimization LIBrary of test problems
Shenglong Zhou, Alain B. Zemkoho, Andrey Tin
This chapter presents the Bilevel Optimization LIBrary of the test problems (BOLIB for short), which contains a collection of test problems, with continuous variables, to help supp…