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We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them.
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Robust estimation and wavelet thresholding in partially linear models
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Fused lasso approach in regression coefficients clustering: learning parameter heterogeneity in data integration
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The landscape of empirical risk for nonconvex losses
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Provable guarantees for gradient-based meta-learning
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Multidimensional linear functional estimation in sparse Gaussian models and robust estimation of the mean
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On the sample complexity of adversarial multi-source PAC learning
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