| LDR | | 02914nam u200433 4500 |
| 001 | | 000000418830 |
| 005 | | 20190215163203 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438091412 |
| 035 | |
▼a (MiAaPQ)AAI10871362 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1511901271093727 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Roychowdhury, Anirban. |
| 245 | 10 |
▼a Robust and Scalable Algorithms for Bayesian Nonparametric Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b The Ohio State University.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 188 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Srinivasan Parthasarathy. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2017. |
| 520 | |
▼a Bayesian nonparametric techniques provide a rich set of tools for modeling complex probabilistic machine learning problems. However the richness comes at the cost of significant complexity of learning and inference for large scale datasets, in a |
| 520 | |
▼a First, we develop fast inference algorithms for sequential models with Bayesian nonparametric priors using small-variance asymptotics, an emerging technique for obtaining scalable combinatorial algorithms from rich probabilistic models. We deriv |
| 520 | |
▼a We start the second section with a novel stick-breaking definition of a certain class of Bayesian nonparametric priors called gamma processes (GP), using its characterization as a completely random measure and attendant Poisson process machinery |
| 520 | |
▼a In the third section, we use concepts from statistical physics to develop a robust Monte Carlo sampler that efficiently traverses the parameter space. Built on the Hamiltonian Monte Carlo framework, our sampler uses a modified Nose-Poincare Hami |
| 520 | |
▼a We continue with an L-BFGS optimization algorithm on Riemannian manifolds that uses stochastic variance reduction techniques for fast convergence with constant step sizes, without resorting to standard linesearch methods, and provide a new conve |
| 520 | |
▼a We finish with a novel technique for learning the mass matrices in Monte Carlo samplers obtained from discretized dynamics that preserve some energy function, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and lea |
| 590 | |
▼a School code: 0168. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a The Ohio State University.
▼b Computer Science and Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0168 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000214
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |