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Machine Learning: Why Do Simple Algorithms Work So Well?
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Machine Learning: Why Do Simple Algorithms Work So Well?. |
| 개인저자 | Jin, Chi. |
| 단체저자명 | University of California, Berkeley. Computer Science. |
| 발행사항 | [S.l.]: University of California, Berkeley., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 158 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781085792608 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Berkeley, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Jordan, Michael I. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | While state-of-the-art machine learning models are deep, large-scale, sequential and highly nonconvex, the backbone of modern learning algorithms are simple algorithms such as stochastic gradient descent, gradient descent with momentum or Q-learning (in the case of reinforcement learning tasks). A basic question endures---why do simple algorithms work so well even in these challenging settings?To answer above question, this thesis focuses on four concrete and fundamental questions:- In nonconvex optimization, can (stochastic) gradient descent or its variants escape saddle points efficiently?- Is gradient descent with momentum provably faster than gradient descent in the general nonconvex setting?- In nonconvex-nonconcave minmax optimization, what is a proper definition of local optima and is gradient descent ascent game-theoretically meaningful?- In reinforcement learning, is Q-learning sample efficient?This thesis provides the first line of provably positive answers to all above questions. In particular, this thesis will show that although the standard versions of these classical algorithms do not enjoy good theoretical properties in the worst case, simple modifications are sufficient to grant them desirable behaviors, which explain the underlying mechanisms behind their favorable performance in practice. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
