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Found 8 papers, 0 papers with code
Distributed Continual Learning with CoCoA in High-dimensional Linear Regression
We consider estimation under scenarios where the signals of interest exhibit change of characteristics over time.
Regularization Trade-offs with Fake Features
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?
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.
Estimation under Model Misspecification with Fake Features
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
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
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
We provide high-probability bounds on the generalization error for both isotropic and correlated Gaussian data as well as sub-gaussian data.
Generalization Error for Linear Regression under Distributed Learning
We consider the setting where the unknowns are distributed over a network of nodes.
