4 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
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…
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective
Yash Travadi, Le Peng, Xuan Bi +2
Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Exi…
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Mochen Yang, Edward McFowland, Gordon Burtch +1
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predi…