1.6k citations · 2.7k across the 28 of their papers we have counts for
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Fast training of large kernel models with delayed projections
Amirhesam Abedsoltan, Siyuan Ma, Parthe Pandit +1
Classical kernel machines have historically faced significant challenges in scaling to large datasets and model sizes--a key ingredient that has driven the success of neural networ…
Reconciling modern machine learning practice and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma +1
Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central t…
Kernel machines that adapt to GPUs for effective large batch training
Siyuan Ma, Mikhail Belkin
Modern machine learning models are typically trained using Stochastic Gradient Descent (SGD) on massively parallel computing resources such as GPUs. Increasing mini-batch size is a…
To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, Soumik Mandal
Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data e…
Diving into the shallows: a computational perspective on large-scale shallow learning
Siyuan Ma, Mikhail Belkin
In this paper we first identify a basic limitation in gradient descent-based optimization methods when used in conjunctions with smooth kernels. An analysis based on the spectral p…