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Found 11 papers, 2 papers with code
Online Parameter-Free Learning of Multiple Low Variance Tasks
We propose a method to learn a common bias vector for a growing sequence of low-variance tasks.
Online-Within-Online Meta-Learning
We study the problem of learning a series of tasks in a fully online Meta-Learning setting.
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs.
Learning To Learn Around A Common Mean
We show that, in this setting, the LTL problem can be reformulated as a Least Squares (LS) problem and we exploit a novel meta- algorithm to efficiently solve it.
Incremental Learning-to-Learn with Statistical Guarantees
In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution.
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization
A standard optimization strategy is based on formulating the problem as one of low rank matrix factorization which, however, leads to a non-convex problem.
Fitting Spectral Decay with the $k$-Support Norm
The spectral $k$-support norm enjoys good estimation properties in low rank matrix learning problems, empirically outperforming the trace norm.
New Perspectives on $k$-Support and Cluster Norms
We note that the spectral box-norm is essentially equivalent to the cluster norm, a multitask learning regularizer introduced by [Jacob et al. 2009a], and which in turn can be interpreted as a perturbation of the spectral k-support norm.
Learning With Dataset Bias in Latent Subcategory Models
However, previous work has highlighted the possible danger of simply training a model from the combined datasets, due to the presence of bias.
Spectral k-Support Norm Regularization
The $k$-support norm has successfully been applied to sparse vector prediction problems.
New Perspectives on k-Support and Cluster Norms
We further extend the $k$-support norm to matrices, and we observe that it is a special case of the matrix cluster norm.
