98 citations · 102 across the 10 of their papers we have counts for
10 papers
The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Victoria Lin, Taedong Yun, Maja Matarić +3
Large language models (LLMs) show potential as simulators of human behavior, offering a scalable way to study responses to interventions. However, because LLMs are trained largely…
Omitted Variable Bias in Language Models Under Distribution Shift
Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael
Despite their impressive performance on a wide variety of tasks, modern language models remain susceptible to distribution shifts, exhibiting brittle behavior when evaluated on dat…
Isolated Causal Effects of Natural Language
Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael
As language technologies become widespread, it is important to understand how changes in language affect reader perceptions and behaviors. These relationships may be formalized as…
Optimizing Language Models for Human Preferences is a Causal Inference Problem
Victoria Lin, Eli Ben-Michael, Louis-Philippe Morency
As large language models (LLMs) see greater use in academic and commercial settings, there is increasing interest in methods that allow language models to generate texts aligned wi…
Text-Transport: Toward Learning Causal Effects of Natural Language
Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael
As language technologies gain prominence in real-world settings, it is important to understand how changes to language affect reader perceptions. This can be formalized as the caus…
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias
Victoria Lin, Louis-Philippe Morency, Dimitrios Dimitriadis +1
In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage. Over time, new models must be trained as new training exam…
SeedBERT: Recovering Annotator Rating Distributions from an Aggregated Label
Aneesha Sampath, Victoria Lin, Louis-Philippe Morency
Many machine learning tasks -- particularly those in affective computing -- are inherently subjective. When asked to classify facial expressions or to rate an individual's attracti…
Toward Multimodal Modeling of Emotional Expressiveness
Victoria Lin, Jeffrey M. Girard, Michael A. Sayette +1
Emotional expressiveness captures the extent to which a person tends to outwardly display their emotions through behavior. Due to the close relationship between emotional expressiv…
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness
Victoria Lin, Jeffrey M. Girard, Louis-Philippe Morency
In recent years, extensive research has emerged in affective computing on topics like automatic emotion recognition and determining the signals that characterize individual emotion…
gfoRmula: An R package for estimating effects of general time-varying treatment interventions via the parametric g-formula
Victoria Lin, Sean McGrath, Zilu Zhang +4
Researchers are often interested in using longitudinal data to estimate the causal effects of hypothetical time-varying treatment interventions on the mean or risk of a future outc…