The industrial sector consumes about one-third of global energy, making them a frequent
target for energy use reduction. Variation in energy usage is observed with weather …

A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

Recent advances in adaptive object detection have achieved compelling results in virtue of
adversarial feature adaptation to mitigate the distributional shifts along the detection …

Adversarial learning has been embedded into deep networks to learn disentangled and
transferable representations for domain adaptation. Existing adversarial domain adaptation …

This book presents a unified theory of random matrices for applications in machine learning,
offering a large-dimensional data vision that exploits concentration and universality …

Bilinear models has been shown to achieve impressive performance on a wide range of
visual tasks, such as semantic segmentation, fine grained recognition and face recognition …

The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

Random matrices now play a role in many areas of theoretical, applied, and computational
mathematics. Therefore, it is desirable to have tools for studying random matrices that are …

Pooling second-order local feature statistics to form a high-dimensional bilinear feature has
been shown to achieve state-of-the-art performance on a variety of fine-grained …