This paper proposes new machine learning methods based on the representation of classes by convex hulls in multidimensional space, and not requiring the computation of convex hulls or triangulation of multiple
Finally, we investigate the proposed algorithms for an important problem in machine learning: the t-distributed stochastic neighbor embedding. Abstract We address the problem of minimizing the sum of a nonconvex, differentiable function and composite functions by DC (Difference of Convex functions) prog...
We propose a combination of machine learning and flux limiting for property-preserving subgrid scale modeling in the context of flux-limited finite volume methods for the one-dimensional shallow-water equations. The numerical fluxes of a conservative target scheme are fitted to the coarse-mesh ...
Many problems in machine learning and other fields can be (re)formulated as linearly constrained separable convex programs. In most of the cases, there are
Foundations and Trends® in Machine Learning(共66册), 这套丛书还有 《Kernels for Vector-Valued Functions》《Model-based Reinforcement Learning》《A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists》《On the Concentration Properties of Interacting Particle Processes》《Spectral Me...
Nan W., et al. “The Value of Collaboration in Convex Machine Learning with Differential Privacy.” 2020 IEEE Symposium on Security and Privacy. 304-317. 联邦学习场景中,在适应度函数平滑、强凸、利普斯特连续的条件下,估算各客户端使用不同隐私预算时最终全局模型的信息损失量。实践中,针对适应...
for high-dimensional problems; approaches to this that effectively optimize over lower-dimensional subspaces simply ignore the actual problems that varying gradient geometries introduce. In contrast, we study non-isotropic clipping and noise addition, developing a principled theoretical approach; the ...
A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm's privacy loss re...
A variety of methods have been proposed in the literature to address this inference problem. As far as we know, none of the objective functions in existing methods is convex. In machine learning and applied statistics, a convex function such as the objective function of support vector machines ...
Since then, he has been an Assistant Professor in the School of Electrical and Electronic Engineering, Nanyang Technological University. His current research interests include machine learning, bioinformatics, and networking. He is a senior member of IEEE. Dr. Huang is an Associate Editor of Neuro...