Search Results for author:
Found 8 papers, 4 papers with code
Pre-registration for Predictive Modeling
Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field.
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
Comparing scalable strategies for generating numerical perspectives
Numerical perspectives help people understand extreme and unfamiliar numbers (e. g., \$330 billion is about \$1, 000 per person in the United States).
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints
Along the way, we refine the study of single-target multiplicity by introducing notions of multiplicity that respect resource constraints -- a feature of many real-world tasks that is not captured by existing notions of predictive multiplicity.
Manipulating and Measuring Model Interpretability
With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models.
Split-door criterion: Identification of causal effects through auxiliary outcomes
We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available.
Scalable Recommendation with Poisson Factorization
This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations.
A Bayesian Approach to Network Modularity
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network.
