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H 2 Adaptive rates for trend filtering using dual certificates - Lecture 2

Auteurs : Van de Geer, Sara (Auteur de la Conférence)
CIRM (Editeur )

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    Résumé : The theory for trend filtering has been developed in a series of papers: Tibshirani [2014], Wang et al. [2016], Sadhanala and Tibshirani [2019], Sadhanala et al. [2017], Guntuboyina et al. [2020], and see Tibshirani [2020] for a very good overview and further references. In this course we will combine the approach of Dalalyan et al. [2017] with dual certificates as given in Candes and Plan [2011]. This allows us to obtain new results and extensions.

    The trend filtering problem in one dimension is as follows. Let $Y\sim \mathcal{N}(f^{0},\ I)$ be a vector of observations with unknown mean $f^{0} :=\mathrm{E}Y$. Let for $f\in \mathbb{R}^{n},$
    $$
    (\triangle f)_{i}\ :=\ f_{i}-f_{i-1},\ i\geq 2,
    $$
    $$
    (\triangle^{2}f)_{i}\ :=\ f_{i}-2f_{i-1}+f_{i-2},\ i\geq 3,
    $$
    $$
    (\triangle^{k}f)_{i}\ :=\ (\triangle(\triangle^{k-1}f))_{i},\ i\geq k+1.
    $$
    Then we consider the estimator
    $$
    \hat{f}\ :=f\min_{\in \mathbb{R}^{n}}\{\Vert Y-f\Vert_{2}^{2}/n+2\lambda\Vert\triangle^{k}f\Vert_{1}\}.
    $$
    Let $S_{0} :=\{j\ :\ (\triangle^{k}f^{0})_{j}\neq 0\}$ and $s_{0} :=|S_{0}|$ its size. We want to prove a "oracle'' type of result: for an appropriate choice of the tuning parameter, and modulo $\log$-factors, with high probability $\Vert\hat{f}-f^{0}\Vert_{2}^{2}/n \lesssim (s_{0}+1)/n$. For this one may apply the general theory for the Lasso. Indeed, the above is a Lasso estimator if we write $f=\Psi b$ where $\Psi\in \mathbb{R}^{n\times n}$ is the "design matrix'' or "dictionary'' and where $b_{j}=(\triangle^{k}f)_{j}, j\geq k+1$. We present the explicit expression for this dictionary and then will notice that the restricted eigenvalue conditions that are typically imposed for Lasso problems do not hold. What we will do instead is use a "dual certificate'' $q$ with index set $\mathcal{D} :=\{k+1,\ .\ .\ .\ ,\ n\}$. We require that $q_{j}=\mathrm{s}\mathrm{i}\mathrm{g}\mathrm{n}(\triangle^{k}f^{0})_{j}$ if $j\in S_{0}$ and such that $|q_{j}|\leq 1-w_{j}$ if $j\in \mathcal{D}\backslash S_{0}$, where $\{w\}_{j\in D\backslash S_{0}}$ is a given set of noise weights. Moreover, we require $q_{k+1}=q_{n}=0.$ We call such $q$ an interpolating vector. We show for an appropriate choice of $\lambda$
    $$
    \Vert\hat{f}-f^{0}\Vert_{2}^{2}/n<\sim(s_{0}+1)/n+n\lambda^{2}\Vert\triangle^{k}q\Vert_{2}^{2}
    $$
    and that the two terms on the right hand side can be up to $\log$ terms of the same order if the elements in $S_{0}$ corresponding to different signs satisfy a minimal distance condition.

    We develop a non-asymptotic theory for the problem and refine the above to sharp oracle results. Moreover, the approach we use allows extensions to higher dimensions and to total variation on graphs. For the case of graphs with cycles the main issue is to determine the noise weights, which can be done by counting the number of times an edge is used when traveling from one node to another. Extensions to other loss functions will be considered as well.

    Keywords : Lasso; total variation; dual certificates

    Codes MSC :
    62J05 - Linear regression
    62J99 - None of the above but in this section


    Ressources complémentaires :
    https://www.cirm-math.fr/RepOrga/2536/Slides/MMS2020Lecture_vandeGeer.pdf


      Informations sur la Vidéo

      Réalisateur : Hennenfent, Guillaume
      Langue : Anglais
      Date de publication : 11/01/2021
      Date de captation : 15/12/2020
      Collection : Research talks ; Probability and Statistics
      Durée : 01:01:36
      Domaine : Probability & Statistics
      Audience : Chercheurs ; Doctorants , Post - Doctorants
      Download : https://videos.cirm-math.fr/2020-12-17_De Geer 2.mp4

    Informations sur la Rencontre Virtuelle

    Nom de la rencontre : Meeting in Mathematical Statistics / Rencontres de statistique mathématique
    Organisateurs de la rencontre : Butucea, Cristina ; Minsker, Stanislav ; Pouet, Christophe ; Spokoiny, Vladimir
    Dates : 14/12/2020 - 18/12/2020
    Année de la rencontre : 2020
    URL Congrès : https://conferences.cirm-math.fr/2536.html

    Citation Data

    DOI : 10.24350/CIRM.V.19692403
    Cite this video as: Van de Geer, Sara (2020). Adaptive rates for trend filtering using dual certificates - Lecture 2.CIRM . Audiovisual resource. doi:10.24350/CIRM.V.19692403
    URI : http://dx.doi.org/10.24350/CIRM.V.19692403


    Voir aussi

    Bibliographie

    1. TIBSHIRANI, Ryan J., et al. Adaptive piecewise polynomial estimation via trend filtering. The Annals of Statistics, 2014, vol. 42, no 1, p. 285-323. - http://dx.doi.org/10.1214/13-AOS1189

    2. E. J. Candes and Y. Plan, "A Probabilistic and RIPless Theory of Compressed Sensing," in IEEE Transactions on Information Theory, vol. 57, no. 11, pp. 7235-7254, Nov. 2011, doi: 10.1109/TIT.2011.2161794. - https://doi.org/10.1109/TIT.2011.2161794

    3. DALALYAN, Arnak S., HEBIRI, Mohamed, LEDERER, Johannes, et al. On the prediction performance of the lasso. Bernoulli, 2017, vol. 23, no 1, p. 552-581. - http://dx.doi.org/10.3150/15-BEJ756

    4. GUNTUBOYINA, Adityanand, LIEU, Donovan, CHATTERJEE, Sabyasachi, et al. Adaptive risk bounds in univariate total variation denoising and trend filtering. The Annals of Statistics, 2020, vol. 48, no 1, p. 205-229. - http://dx.doi.org/10.1214/18-AOS1799

    5. SADHANALA, Veeranjaneyulu, TIBSHIRANI, Ryan J., et al. Additive models with trend filtering. The Annals of Statistics, 2019, vol. 47, no 6, p. 3032-3068. - http://dx.doi.org/10.1214/19-AOS1833

    6. SADHANALA, Veeranjaneyulu, WANG, Yu-Xiang, SHARPNACK, James L., et al. Higher-order total variation classes on grids: Minimax theory and trend filtering methods. In : Advances in Neural Information Processing Systems. 2017. p. 5800-5810. - https://papers.nips.cc/paper/2017/file/3e60e09c222f206c725385f53d7e567c-Paper.pdf

    7. TIBSHIRANI, Ryan J. Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. arXiv preprint arXiv:2003.03886, 2020. - https://arxiv.org/abs/2003.03886

    8. WANG, Yu-Xiang, SHARPNACK, James, SMOLA, Alexander J., et al. Trend filtering on graphs. The Journal of Machine Learning Research, 2016, vol. 17, no 1, p. 3651-3691. - https://dl.acm.org/doi/pdf/10.5555/2946645.3007058

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