11 citations · 20 across the 5 of their papers we have counts for
7 papers
Distributed Continual Learning with CoCoA in High-dimensional Linear Regression
Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
We consider estimation under scenarios where the signals of interest exhibit change of characteristics over time. In particular, we consider the continual learning problem where di…
Regularization Trade-offs with Fake Features
Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
Recent successes of massively overparameterized models have inspired a new line of work investigating the underlying conditions that enable overparameterized models to generalize w…
Continual Learning with Distributed Optimization: Does CoCoA Forget?
Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
We focus on the continual learning problem where the tasks arrive sequentially and the aim is to perform well on the newly arrived task without performance degradation on the previ…
Estimation under Model Misspecification with Fake Features
Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
We consider estimation under model misspecification where there is a model mismatch between the underlying system, which generates the data, and the model used during estimation. W…
Model Mismatch Trade-offs in LMMSE Estimation
Martin Hellkvist, Ayça Özçelikkale
We consider a linear minimum mean squared error (LMMSE) estimation framework with model mismatch where the assumed model order is smaller than that of the underlying linear system…
Linear Regression with Distributed Learning: A Generalization Error Perspective
Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
Distributed learning provides an attractive framework for scaling the learning task by sharing the computational load over multiple nodes in a network. Here, we investigate the per…