A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening

NeurIPS 2017 Kevin LinJames L. SharpnackAlessandro RinaldoRyan J. Tibshirani

In the 1-dimensional multiple changepoint detection problem, we derive a new fast error rate for the fused lasso estimator, under the assumption that the mean vector has a sparse number of changepoints. This rate is seen to be suboptimal (compared to the minimax rate) by only a factor of $\log\log{n}$... (read more)

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