170 citations · 909 across the 66 of their papers we have counts for
9 papers · 1 filter
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning
Yuege Xie, Bobby Shi, Hayden Schaeffer +1
Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between vari…
Learning to Forecast Dynamical Systems from Streaming Data
Dimitris Giannakis, Amelia Henriksen, Joel A. Tropp +1
Kernel analog forecasting (KAF) is a powerful methodology for data-driven, non-parametric forecasting of dynamically generated time series data. This approach has a rigorous founda…
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Xiaoxia Wu, Yuege Xie, Simon Du +1
We propose a computationally-friendly adaptive learning rate schedule, "AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descen…
Arbitrary-length analogs to de Bruijn sequences
Abhinav Nellore, Rachel Ward
Let be a length- cyclic sequence of characters from a size- alphabet such that the number of occurrences of any length- string on …
Johnson-Lindenstrauss Embeddings with Kronecker Structure
Stefan Bamberger, Felix Krahmer, Rachel Ward
We prove the Johnson-Lindenstrauss property for matrices where has the restricted isometry property and is a diagonal matrix containing the entries of a Kronecker…
The Hanson-Wright Inequality for Random Tensors
Stefan Bamberger, Felix Krahmer, Rachel Ward
We provide moment bounds for expressions of the type where denotes the Kronecker pro…
Bootstrapping the error of Oja's algorithm
Robert Lunde, Purnamrita Sarkar, Rachel Ward
We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the da…
Generalization Bounds for Sparse Random Feature Expansions
Abolfazl Hashemi, Hayden Schaeffer, Robert Shi +3
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach…
Streaming k-PCA: Efficient guarantees for Oja's algorithm, beyond rank-one updates
De Huang, Jonathan Niles-Weed, Rachel Ward
We analyze Oja's algorithm for streaming -PCA and prove that it achieves performance nearly matching that of an optimal offline algorithm. Given access to a sequence of i.i.d. $…