5 citations · 11 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning to Represent Individual Differences for Choice Decision Making
Yan-Ying Chen, Yue Weng, Alexandre Filipowicz +7
Human decision making can be challenging to predict because decisions are affected by a number of complex factors. Adding to this complexity, decision-making processes can differ c…
ConjointNet: Enhancing Conjoint Analysis for Preference Prediction with Representation Learning
Yanxia Zhang, Francine Chen, Shabnam Hakimi +9
Understanding consumer preferences is essential to product design and predicting market response to these new products. Choice-based conjoint analysis is widely used to model user…
Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression
Nikos Arechiga, Francine Chen, Yan-Ying Chen +4
We consider the problem of learning free-form symbolic expressions from raw data, such as that produced by an experiment in any scientific domain. Accurate and interpretable models…
Machine learning reveals how personalized climate communication can both succeed and backfire
Totte Harinen, Alexandre Filipowicz, Shabnam Hakimi +3
Different advertising messages work for different people. Machine learning can be an effective way to personalise climate communications. In this paper we use machine learning to r…