资源分类
》 资源 》论文》A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening

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

2020-02-10 | |  50 |   38 |   0

Abstract 

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. Our proof technique is centered around a novel construction that we call a lower interpolant. We extend our results to misspecified models and exponential family distributions. We also describe the implications of our error analysis for the approximate screening of changepoints.

上一篇：Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems

下一篇：Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...