明日讲座速递 | 大湾区数据科学与统计云讲堂
讲座信息
主题:
Blessing of Online Tensor Learning and Computational and Statistical Tradeoffs
嘉宾:李竞阳
地点:腾讯会议:846-3927-0358
时间:2023年04月01日 19:00
内容摘要
We investigate a generalized framework in online setting to estimate a latent low-rank tensor. This framework covers both linear and generalized linear models, and can easily handle continuous or categorical variables in online setting. Meanwhile, we also consider online tensor completion and online binary tensor learning. We propose the online Riemannian gradient descent algorithm, which is proved to converge linearly and can recover the low-rank part under suitable conditions in all the applications. Moreover, a sharp entry-wise error bound is provided in the online tensor completion. To the best of our knowledge, our results are the first that take noise into consideration in the online low-rank tensor (even low-rank matrix) recovery task. In the noise case, a surprising computational and statistical tradeoff is observed: a larger stepsize leads to a faster convergence speed while the output has a larger statistical error; a smaller stepsize gives rise to a statistical optimal estimator but has slower convergence.
嘉宾简介
李竞阳,香港科技大学在读博士生,研究兴趣包含矩阵/张量数据分析,信号处理,联邦学习,差分隐私等。
全系列介绍
统计之都(Capital of Statistics,简称 COS)成立于 2006 年,是一个旨在推广与应用统计学、数据科学知识的公益性网站和社区。
统计之都以专业、人本、正直、团结的理念尝试推动统计和数据科学在中国的发展,促进各行业的创新和繁荣。
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版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。