26 citations · 76 across the 11 of their papers we have counts for
4 papers · 1 filter
Scaling Laws Do Not Scale
Fernando Diaz, Michael Madaio
Recent work has advocated for training AI models on ever-larger datasets, arguing that as the size of a dataset increases, the performance of a model trained on that dataset will c…
Striving for data-model efficiency: Identifying data externalities on group performance
Esther Rolf, Ben Packer, Alex Beutel +1
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predict…
Analyzing the Effect of Sampling in GNNs on Individual Fairness
Rebecca Salganik, Fernando Diaz, Golnoosh Farnadi
Graph neural network (GNN) based methods have saturated the field of recommender systems. The gains of these systems have been significant, showing the advantages of interpreting d…
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Divya Shanmugam, Samira Shabanian, Fernando Diaz +2
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practice…