Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Claici, Sebastian"'
Autor:
Claici, Sebastian.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 169-187).
The typical mac
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 169-187).
The typical mac
Externí odkaz:
https://hdl.handle.net/1721.1/127014
We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average app
Externí odkaz:
http://arxiv.org/abs/2007.06168
We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the data distribution from a specified set of distributions. We illustrate
Externí odkaz:
http://arxiv.org/abs/1912.07729
Autor:
Monteiller, Pierre, Claici, Sebastian, Chien, Edward, Mirzazadeh, Farzaneh, Solomon, Justin, Yurochkin, Mikhail
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures. This issue arises due to invariance of the posterior under actions
Externí odkaz:
http://arxiv.org/abs/1911.02053
The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora. Past distances between documents suffer from either an inability to incorporate semantic similarities between words or from scalabili
Externí odkaz:
http://arxiv.org/abs/1906.10827
Publikováno v:
SIGGRAPH Asia 2018, Dec 2018, Tokyo, Japan. 37, 2018
We propose a technique for interpolating between probability distributions on discrete surfaces, based on the theory of optimal transport. Unlike previous attempts that use linear programming, our method is based on a dynamical formulation of quadrat
Externí odkaz:
http://arxiv.org/abs/1809.07083
The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification. Coresets provide a principled way of summarizing a large dataset via a smaller one that is guaranteed to match the performance of
Externí odkaz:
http://arxiv.org/abs/1805.07412
We present a stochastic algorithm to compute the barycenter of a set of probability distributions under the Wasserstein metric from optimal transport. Unlike previous approaches, our method extends to continuous input distributions and allows the sup
Externí odkaz:
http://arxiv.org/abs/1802.05757
Autor:
Claici, Sebastian
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-66).
In this thesis, we present a
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-66).
In this thesis, we present a
Externí odkaz:
http://hdl.handle.net/1721.1/107376
Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a s
Externí odkaz:
http://arxiv.org/abs/1705.07443