9 citations · 35 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Mirror Descent on Reproducing Kernel Banach Spaces
Akash Kumar, Mikhail Belkin, Parthe Pandit
Recent advances in machine learning have led to increased interest in reproducing kernel Banach spaces (RKBS) as a more general framework that extends beyond reproducing kernel Hil…
Eigenvectors of the De Bruijn Graph Laplacian: A Natural Basis for the Cut and Cycle Space
Anthony Philippakis, Neil Mallinar, Parthe Pandit +1
We study the Laplacian of the undirected De Bruijn graph over an alphabet of order . While the eigenvalues of this Laplacian were found in 1998 by Delorme and Tillich [1], a…
Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime
Parthe Pandit, Zhichao Wang, Yizhe Zhu
Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much of the focus thus far has been o…
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Neil Mallinar, Daniel Beaglehole, Libin Zhu +3
Neural networks trained to solve modular arithmetic tasks exhibit grokking, a phenomenon where the test accuracy starts improving long after the model achieves 100% training accura…
On the Nystrom Approximation for Preconditioning in Kernel Machines
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher +1
Kernel methods are a popular class of nonlinear predictive models in machine learning. Scalable algorithms for learning kernel models need to be iterative in nature, but convergenc…