5 citations · 13 across the 8 of their papers we have counts for
19 papers
How catastrophic can catastrophic forgetting be in linear regression?
Itay Evron, Edward Moroshko, Rachel Ward +2
To better understand catastrophic forgetting, we study fitting an overparameterized linear model to a sequence of tasks with different input distributions. We analyze how much the…
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…
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…
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. $…