Statistical Optimal Transport: École d'Été de Probabilités de Saint-Flour XLIX - 2019
暫譯: 統計最優傳輸：聖佛魯概率夏季學院 XLIX - 2019
Chewi, Sinho, Niles-Weed, Jonathan, Rigollet, Philippe
- 出版商: Springer
- 出版日期: 2025-04-11
- 售價: $3,280
- 貴賓價: 9.5 折 $3,116
- 語言: 英文
- 頁數: 260
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031851595
- ISBN-13: 9783031851599
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相關主題
商品描述
This monograph aims to offer a concise introduction to optimal transport, quickly transitioning to its applications in statistics and machine learning. It is primarily tailored for students and researchers in these fields, yet it remains accessible to a broader audience of applied mathematicians and computer scientists. Each chapter is complemented with exercises for the reader to test their understanding. As such, this monograph is suitable for a graduate course on the topic of statistical optimal transport.
商品描述(中文翻譯)
本專著旨在提供對最優運輸的簡明介紹，並迅速轉向其在統計學和機器學習中的應用。它主要針對這些領域的學生和研究人員，但對於更廣泛的應用數學家和計算機科學家也保持可讀性。每一章都附有練習題，供讀者檢測自己的理解。因此，本專著適合用作有關統計最優運輸主題的研究生課程。
作者簡介
Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University. He obtained his PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023, under the supervision of Philippe Rigollet. He works broadly on the mathematics of machine learning and statistics, with a focus on applications of optimal transport to computational problems arising in those fields. He is currently writing a book on log-concave sampling. Jonathan Niles-Weed is an Associate Professor of Mathematics and Data Science at New York University. He studies mathematical statistics, the mathematics of data science, and applications of optimal transport in statistics, probability, and machine learning. He holds a PhD from the Massachusetts Institute of Technology and is the recipient of a Sloan Fellowship in Mathematics, an NSF CAREER award, the 2023 Tweedie New Researcher Award from the Institute for Mathematical Statistics, and the 2024 Early Career Prize from the SIAM Activity Group on Data Science. Philippe Rigollet is the Cecil and Ida Green Distinguished Professor of Mathematics at MIT, where he serves as Chair of the Applied Mathematics Committee. He works at the intersection of statistics, machine learning, and optimization, focusing primarily on the design and analysis of efficient statistical methods. His current research is on statistical optimal transport and the mathematical theory behind transformers. His research has been recognized by the CAREER award from the National Science Foundation and a Best Paper Award at the Conference on Learning Theory in 2013 for his pioneering work on statistical-to-computational tradeoffs. He is an elected fellow of the Institute of Mathematical Statistics and gave a Medallion lecture at the Joint Statistical Meetings in 2021.
作者簡介(中文翻譯)
Sinho Chewi 是耶魯大學統計學與數據科學的助理教授。他於2023年在麻省理工學院獲得數學與統計學博士學位，指導教授為 Philippe Rigollet。他的研究範圍廣泛，涵蓋機器學習與統計學的數學，特別專注於最優傳輸在這些領域中出現的計算問題的應用。他目前正在撰寫一本關於對數凹取樣的書籍。 Jonathan Niles-Weed 是紐約大學數學與數據科學的副教授。他研究數學統計、數據科學的數學以及最優傳輸在統計學、機率論和機器學習中的應用。他擁有麻省理工學院的博士學位，並獲得數學斯隆獎學金、國家科學基金會的 CAREER 獎、2023年數學統計學會的 Tweedie 新研究者獎，以及2024年 SIAM 數據科學活動小組的早期職業獎。 Philippe Rigollet 是麻省理工學院的 Cecil 和 Ida Green 傑出數學教授，並擔任應用數學委員會的主席。他的研究位於統計學、機器學習和優化的交叉點，主要專注於高效統計方法的設計與分析。他目前的研究集中在統計最優傳輸及其背後的數學理論，特別是變壓器的數學理論。他的研究曾獲得國家科學基金會的 CAREER 獎，以及2013年學習理論會議的最佳論文獎，以表彰他在統計與計算權衡方面的開創性工作。他是數學統計學會的當選會士，並於2021年在聯合統計會議上發表了獎章講座。
