Support vector machine (SVM) is an optimal margin based classification technique in
machine learning. SVM is a binary linear classifier which has been extended to non-linear …

Linear classification is a useful tool in machine learning and data mining. For some data in a
rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …

We identify and formalize a fundamental gradient descent phenomenon resulting in a
learning proclivity in over-parameterized neural networks. Gradient Starvation arises when …

Stochastic gradient descent is popular for large scale optimization but has slow
convergence asymptotically due to the inherent variance. To remedy this problem, we …

Abstract Stochastic Gradient Descent (SGD) has become popular for solving large scale
supervised machine learning optimization problems such as SVM, due to their strong …

In the problem of domain generalization (DG), there are labeled training data sets from
several related prediction problems, and the goal is to make accurate predictions on future …

A new edition of a graduate-level machine learning textbook that focuses on the analysis
and theory of algorithms. This book is a general introduction to machine learning that can …

Support vector machines (SVM), as one of effective and popular classification tools, have
been widely applied in various fields. However, they may incur prohibitive computational …

This paper presents a simple yet effective supervised deep hash approach that constructs
binary hash codes from labeled data for large-scale image search. We assume that the …

Although disentangled representations are often said to be beneficial for downstream tasks,
current empirical and theoretical understanding is limited. In this work, we provide evidence …