Search Results for author: Ayça Özçelikkale

Found 8 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

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.

Model Mismatch Trade-offs in LMMSE Estimation

no code implementations25 May 2021 Martin Hellkvist, Ayça Özçelikkale

By modelling the regressors of the underlying system as random variables, we analyze the average behaviour of the mean squared error (MSE).

Chance-Constrained Active Inference

no code implementations17 Feb 2021 Thijs van de Laar, Ismail Senoz, Ayça Özçelikkale, Henk Wymeersch

We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing e. g., for a trade-off between robust control and empirical chance constraint violation.

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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