2 citations · 2 across the 1 of their papers we have counts for
4 papers
EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference with ML-Generated Variables
Gordon Burtch, Edward McFowland, Mochen Yang +1
Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measur…
Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach
Xuan Bi, Yaqiong Wang, Gediminas Adomavicius +1
With the advancement of machine learning and artificial intelligence technologies, recommender systems have been increasingly used across a vast variety of platforms to efficiently…
Robustness is Important: Limitations of LLMs for Data Fitting
Hejia Liu, Mochen Yang, Gediminas Adomavicius
Large Language Models (LLMs) are being applied in a wide array of settings, well beyond the typical language-oriented use cases. In particular, LLMs are increasingly used as a plug…
De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
Robin Burke, Gediminas Adomavicius, Toine Bogers +7
Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due…