Search Results for author: Anders Ahlén

Found 7 papers, 0 papers with code

Distributed Continual Learning with CoCoA in High-dimensional Linear Regression

no code implementations4 Dec 2023 Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén

We consider estimation under scenarios where the signals of interest exhibit change of characteristics over time.

Continual Learning regression

Regularization Trade-offs with Fake Features

no code implementations1 Dec 2022 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 well.


Continual Learning with Distributed Optimization: Does CoCoA Forget?

no code implementations30 Nov 2022 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 previously seen tasks.

Continual Learning Distributed Optimization

Risk assessment and optimal allocation of security measures under stealthy false data injection attacks

no code implementations11 Jul 2022 Sribalaji C. Anand, André M. H. Teixeira, Anders Ahlén

The numerical example also illustrates that the security allocation using the Value-at-risk, and the impact on the nominal system may have different outcomes: thereby depicting the benefit of using risk metrics.

Estimation under Model Misspecification with Fake Features

no code implementations7 Mar 2022 Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén

Our results show that fake features can significantly improve the estimation performance, even though they are not correlated with the features in the underlying system.

Linear Regression with Distributed Learning: A Generalization Error Perspective

no code implementations22 Jan 2021 Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén

We provide high-probability bounds on the generalization error for both isotropic and correlated Gaussian data as well as sub-gaussian data.


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