eprintid: 1470010
rev_number: 51
eprint_status: archive
userid: 608
dir: disk0/01/47/00/10
datestamp: 2018-05-08 13:14:39
lastmod: 2021-10-10 22:38:24
status_changed: 2018-05-08 13:14:39
type: proceedings_section
metadata_visibility: show
creators_name: Jitkrittum, W
creators_name: Gretton, A
creators_name: Heess, N
creators_name: Eslami, SMA
creators_name: Lakshminarayanan, B
creators_name: Sejdinovic, D
creators_name: Szabó, Z
title: Kernel-based just-in-time learning for passing expectation propagation messages
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D76
divisions: B04
divisions: C05
divisions: F48
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not have an analytic expression. We use kernel-based regression, which is trained on a set of probability distributions representing the incoming messages, and the associated outgoing messages. The kernel approach has two main advantages: first, it is fast, as it is implemented using a novel two-layer random feature representation of the input message distributions; second, it has principled uncertainty estimates, and can be cheaply updated online, meaning it can request and incorporate new training data when it encounters inputs on which it is uncertain. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets (logistic regression for a variety of classification problems), where it is essential to accurately assess uncertainty and to efficiently and robustly update the message operator.
date: 2015-07-12
publisher: AUAI Press
official_url: http://auai.org/uai2015/proceedings/papers/235.pdf
vfaculties: VFLS
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1033814
lyricists_name: Gretton, Arthur
lyricists_name: Jitkrittum, Wittawat
lyricists_name: Lakshminarayanan, Balaji
lyricists_name: Szabo, Zoltan
lyricists_id: AGRET87
lyricists_id: JWITT76
lyricists_id: BLAKS90
lyricists_id: ZSZAB96
actors_name: Szabo, Zoltan
actors_id: ZSZAB96
actors_role: owner
full_text_status: public
series: Conference on Uncertainty in Artificial Intelligence (UAI'15 )
publication: Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015
volume: 31
place_of_pub: Virginia, USA
pagerange: 405-414
event_title: Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 )
institution: Conference on Uncertainty in Artificial Intelligence (UAI)
book_title: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 )
editors_name: Meila, Marina
editors_name: Heskes, Tom
citation:        Jitkrittum, W;    Gretton, A;    Heess, N;    Eslami, SMA;    Lakshminarayanan, B;    Sejdinovic, D;    Szabó, Z;      (2015)    Kernel-based just-in-time learning for passing expectation propagation messages.                     In: Meila, Marina and Heskes, Tom, (eds.) Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 ).  (pp. pp. 405-414).  AUAI Press: Virginia, USA.       Green open access   
 
document_url: https://discovery-pp.ucl.ac.uk/id/eprint/1470010/1/jitkrittum15kernel.pdf
document_url: https://discovery-pp.ucl.ac.uk/id/eprint/1470010/6/jitkrittum15kernel_spotlight.pdf
document_url: https://discovery-pp.ucl.ac.uk/id/eprint/1470010/11/jitkrittum15kernel_poster.pdf