TY  - GEN
KW  - Optimization
KW  -  Discrete Choice Models
KW  -  Stochastic Algorithms
KW  -  Adaptive Batch Size
T3  - iss Transport Research Conference
A1  - Lederrey, Gael
A1  - Lurkin, Virginie
A1  - Hillel, Tim
A1  - Bierlaire, Michel
CY  - Ascona, Switzerland
UR  - https://www.strc.ch/2019.php
PB  - Institute for Economic Research, Università della Svizzera italiana
ID  - discovery10174125
N2  - 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.
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
Y1  - 2019/05//
AV  - public
TI  - Stochastic Optimization with Adaptive Batch Size:
Discrete Choice Models as a Case Study
ER  -