Information and Inference-A Journal of the Ima最新文献_第7页

OUP accepted manuscript OUP接受稿件

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2022-01-01 DOI: 10.1093/imaiai/iaab028

引用次数: 0

Third-order moment varieties of linear non-Gaussian graphical models 线性非高斯图形模型的三阶矩变化

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-12-20 DOI: 10.1093/imaiai/iaad007

Carlos Am'endola, M. Drton, Alexandros Grosdos, R. Homs, Elina Robeva

引用次数: 3

From the simplex to the sphere: faster constrained optimization using the Hadamard parametrization 从单纯形到球面:使用Hadamard参数化的更快约束优化

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-12-10 DOI: 10.1093/imaiai/iaad017

Qiuwei Li, Daniel Mckenzie, W. Yin

{"title":"From the simplex to the sphere: faster constrained optimization using the Hadamard parametrization","authors":"Qiuwei Li, Daniel Mckenzie, W. Yin","doi":"10.1093/imaiai/iaad017","DOIUrl":"https://doi.org/10.1093/imaiai/iaad017","url":null,"abstract":"\u0000 The standard simplex in $mathbb{R}^{n}$, also known as the probability simplex, is the set of nonnegative vectors whose entries sum up to 1. It frequently appears as a constraint in optimization problems that arise in machine learning, statistics, data science, operations research and beyond. We convert the standard simplex to the unit sphere and thus transform the corresponding constrained optimization problem into an optimization problem on a simple, smooth manifold. We show that Karush-Kuhn-Tucker points and strict-saddle points of the minimization problem on the standard simplex all correspond to those of the transformed problem, and vice versa. So, solving one problem is equivalent to solving the other problem. Then, we propose several simple, efficient and projection-free algorithms using the manifold structure. The equivalence and the proposed algorithm can be extended to optimization problems with unit simplex, weighted probability simplex or $ell _{1}$-norm sphere constraints. Numerical experiments between the new algorithms and existing ones show the advantages of the new approach. Open source code is available at https://github.com/DanielMckenzie/HadRGD.","PeriodicalId":45437,"journal":{"name":"Information and Inference-A Journal of the Ima","volume":"66 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78101118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Wavelet invariants for statistically robust multi-reference alignment. 用于统计稳健多参考对齐的小波不变式。

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-12-01 Epub Date: 2020-08-13 DOI: 10.1093/imaiai/iaaa016

Matthew Hirn, Anna Little

引用次数: 0

Erratum to: Subspace clustering using ensembles of K>-subspaces 对使用K>-子空间集合的子空间聚类的勘误

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-10-15 DOI: 10.1093/imaiai/iaab026

J. Lipor, D. Hong, Yan Shuo Tan, L. Balzano

引用次数: 1

Estimating location parameters in sample-heterogeneous distributions 估计样本异质分布中的位置参数

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-06-03 DOI: 10.1093/IMAIAI/IAAB013

Ankit Pensia, Varun Jog, Po-Ling Loh

引用次数: 3

Compressive learning with privacy guarantees 具有隐私保证的压缩学习

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-05-15 DOI: 10.1093/IMAIAI/IAAB005

Antoine Chatalic, V. Schellekens, F. Houssiau, Y. de Montjoye, L. Jacques, R. Gribonval

{"title":"Compressive learning with privacy guarantees","authors":"Antoine Chatalic, V. Schellekens, F. Houssiau, Y. de Montjoye, L. Jacques, R. Gribonval","doi":"10.1093/IMAIAI/IAAB005","DOIUrl":"https://doi.org/10.1093/IMAIAI/IAAB005","url":null,"abstract":"\u0000 This work addresses the problem of learning from large collections of data with privacy guarantees. The compressive learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, called a sketch vector, from which the learning task is then performed. We provide sharp bounds on the so-called sensitivity of this sketching mechanism. This allows us to leverage standard techniques to ensure differential privacy—a well-established formalism for defining and quantifying the privacy of a random mechanism—by adding Laplace of Gaussian noise to the sketch. We combine these standard mechanisms with a new feature subsampling mechanism, which reduces the computational cost without damaging privacy. The overall framework is applied to the tasks of Gaussian modeling, k-means clustering and principal component analysis, for which sharp privacy bounds are derived. Empirically, the quality (for subsequent learning) of the compressed representation produced by our mechanism is strongly related with the induced noise level, for which we give analytical expressions.","PeriodicalId":45437,"journal":{"name":"Information and Inference-A Journal of the Ima","volume":"51 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90454586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Double robust semi-supervised inference for the mean: selection bias under MAR labeling with decaying overlap 均值的双鲁棒半监督推理:重叠衰减的MAR标记下的选择偏差

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-04-14 DOI: 10.1093/imaiai/iaad021

Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic

{"title":"Double robust semi-supervised inference for the mean: selection bias under MAR labeling with decaying overlap","authors":"Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic","doi":"10.1093/imaiai/iaad021","DOIUrl":"https://doi.org/10.1093/imaiai/iaad021","url":null,"abstract":"\u0000 Semi-supervised (SS) inference has received much attention in recent years. Apart from a moderate-sized labeled data, $mathcal L$, the SS setting is characterized by an additional, much larger sized, unlabeled data, $mathcal U$. The setting of $|mathcal U |gg |mathcal L |$, makes SS inference unique and different from the standard missing data problems, owing to natural violation of the so-called ‘positivity’ or ‘overlap’ assumption. However, most of the SS literature implicitly assumes $mathcal L$ and $mathcal U$ to be equally distributed, i.e., no selection bias in the labeling. Inferential challenges in missing at random type labeling allowing for selection bias, are inevitably exacerbated by the decaying nature of the propensity score (PS). We address this gap for a prototype problem, the estimation of the response’s mean. We propose a double robust SS mean estimator and give a complete characterization of its asymptotic properties. The proposed estimator is consistent as long as either the outcome or the PS model is correctly specified. When both models are correctly specified, we provide inference results with a non-standard consistency rate that depends on the smaller size $|mathcal L |$. The results are also extended to causal inference with imbalanced treatment groups. Further, we provide several novel choices of models and estimators of the decaying PS, including a novel offset logistic model and a stratified labeling model. We present their properties under both high- and low-dimensional settings. These may be of independent interest. Lastly, we present extensive simulations and also a real data application.","PeriodicalId":45437,"journal":{"name":"Information and Inference-A Journal of the Ima","volume":"24 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78754638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Topological information retrieval with dilation-invariant bottleneck comparative measures 基于扩展不变瓶颈比较测度的拓扑信息检索

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-04-04 DOI: 10.1093/imaiai/iaad022

Athanasios Vlontzos, Yueqi Cao, Luca Schmidtke, Bernhard Kainz, Anthea Monod

{"title":"Topological information retrieval with dilation-invariant bottleneck comparative measures","authors":"Athanasios Vlontzos, Yueqi Cao, Luca Schmidtke, Bernhard Kainz, Anthea Monod","doi":"10.1093/imaiai/iaad022","DOIUrl":"https://doi.org/10.1093/imaiai/iaad022","url":null,"abstract":"\u0000 Appropriately representing elements in a database so that queries may be accurately matched is a central task in information retrieval; recently, this has been achieved by embedding the graphical structure of the database into a manifold in a hierarchy-preserving manner using a variety of metrics. Persistent homology is a tool commonly used in topological data analysis that is able to rigorously characterize a database in terms of both its hierarchy and connectivity structure. Computing persistent homology on a variety of embedded datasets reveals that some commonly used embeddings fail to preserve the connectivity. We show that those embeddings which successfully retain the database topology coincide in persistent homology by introducing two dilation-invariant comparative measures to capture this effect: in particular, they address the issue of metric distortion on manifolds. We provide an algorithm for their computation that exhibits greatly reduced time complexity over existing methods. We use these measures to perform the first instance of topology-based information retrieval and demonstrate its increased performance over the standard bottleneck distance for persistent homology. We showcase our approach on databases of different data varieties including text, videos and medical images.","PeriodicalId":45437,"journal":{"name":"Information and Inference-A Journal of the Ima","volume":"58 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77142316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi-scale vector quantization with reconstruction trees 基于重构树的多尺度矢量量化

IF 1.6 4区数学

Information and Inference-A Journal of the Ima Pub Date : 2021-02-01 DOI: 10.1093/imaiai/iaaa004

Enrico Cecini;Ernesto De Vito;Lorenzo Rosasco

引用次数: 0