5 citations · 11 across the 8 of their papers we have counts for
8 papers
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…
Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles
Keiichi Namikoshi, David A. Shamma, Rumen Iliev +5
Behavior change interventions are important to coordinate societal action across a wide array of important applications, including the adoption of electrified vehicles to reduce em…
Using LLMs to Model the Beliefs and Preferences of Targeted Populations
Keiichi Namikoshi, Alex Filipowicz, David A. Shamma +3
We consider the problem of aligning a large language model (LLM) to model the preferences of a human population. Modeling the beliefs, preferences, and behaviors of a specific popu…
Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots
Alexandre Filipowicz, Scott Carter, Nayeli Bravo +6
Visualizations are common methods to convey information but also increasingly used to spread misinformation. It is therefore important to understand the factors people use to inter…
Understanding and Shifting Preferences for Battery Electric Vehicles
Nikos Arechiga, Francine Chen, Rumen Iliev +12
Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individua…
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…