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020 ▼a 9780438026636
035 ▼a (MiAaPQ)AAI10814646
035 ▼a (MiAaPQ)cornellgrad:10809
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Yuan, Yang.
24510 ▼a Provable and Practical Algorithms for Non-Convex Problems in Machine Learning.
260 ▼a [S.l.] : ▼b Cornell University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 204 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Robert David Kleinberg.
5021 ▼a Thesis (Ph.D.)--Cornell University, 2018.
520 ▼a Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between theory and practice. On one hand, a simple algorithm like stochastic gradient descent (SG
520 ▼a This dissertation is about bridging the gap between theory and practice from two directions. The first direction is "practice to theory", i.e., to explain and analyze the existing algorithms and empirical observations in machine learning. Along
520 ▼a The other direction is "theory to practice", i.e., using theoretical tools to obtain new, better and practical algorithms. Along this direction, we introduce a new algorithm Harmonica that uses Fourier analysis and compressed sensing for tuning
590 ▼a School code: 0058.
650 4 ▼a Computer science.
650 4 ▼a Artificial intelligence.
690 ▼a 0984
690 ▼a 0800
71020 ▼a Cornell University. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0058
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998133 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자