eprintid: 10174125
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/17/41/25
datestamp: 2023-10-10 13:12:28
lastmod: 2023-10-10 13:12:28
status_changed: 2023-10-10 13:12:28
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Lederrey, Gael
creators_name: Lurkin, Virginie
creators_name: Hillel, Tim
creators_name: Bierlaire, Michel
title: Stochastic Optimization with Adaptive Batch Size:
Discrete Choice Models as a Case Study
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Optimization, Discrete Choice Models, Stochastic Algorithms, Adaptive Batch Size
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: The 2.5 quintillion bytes of data created each day brings new opportunities, but also new
stimulating challenges for the discrete choice community. Opportunities because more and more
new and larger data sets will undoubtedly become available in the future. Challenging because
insights can only be discovered if models can be estimated, which is not simple on these large
datasets.
In this paper, inspired by the good practices and the intensive use of stochastic gradient methods
in the ML field, we introduce the algorithm called Window Moving Average - Adaptive Batch
Size (WMA-ABS) which is used to improve the efficiency of stochastic second-order methods.
We present preliminary results that indicate that our algorithms outperform the standard secondorder methods, especially for large datasets. It constitutes a first step to show that stochastic
algorithms can finally find their place in the optimization of Discrete Choice Models.
date: 2019-05
date_type: published
publisher: Institute for Economic Research, Università della Svizzera italiana
official_url: https://www.strc.ch/2019.php
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2040001
lyricists_name: Hillel, Timothy
lyricists_id: THILL50
actors_name: Hillel, Timothy
actors_id: THILL50
actors_role: owner
full_text_status: public
pres_type: paper
series: iss Transport Research Conference
volume: 2019
place_of_pub: Ascona, Switzerland
event_title: 19th Swiss Transport Research Conference
event_location: Ascona, Switzerland
event_dates: 15 May 2019 - 17 Sep 2019
book_title: 19th Swiss Transport Research Conference
editors_name: Scherer, Patrick
citation:        Lederrey, Gael;    Lurkin, Virginie;    Hillel, Tim;    Bierlaire, Michel;      (2019)    Stochastic Optimization with Adaptive Batch Size: Discrete Choice Models as a Case Study.                     In: Scherer, Patrick, (ed.) 19th Swiss Transport Research Conference.    Institute for Economic Research, Università della Svizzera italiana: Ascona, Switzerland.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10174125/1/Lederrey_EtAl%20%281%29.pdf