7 papers
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…
Asymptotic convexity of wide and shallow neural networks
Vivek Borkar, Parthe Pandit
For a simple model of shallow and wide neural networks, we show that the epigraph of its input-output map as a function of the network parameters approximates epigraph of a. convex…
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…
Feature maps for the Laplacian kernel and its generalizations
Sudhendu Ahir, Parthe Pandit
Recent applications of kernel methods in machine learning have seen a renewed interest in the Laplacian kernel, due to its stability to the bandwidth hyperparameter in comparison t…
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…