1 citations · 1 across the 3 of their papers we have counts for
5 papers
Learning Tensor Representations for Meta-Learning
Samuel Deng, Yilin Guo, Daniel Hsu +1
We introduce a tensor-based model of shared representation for meta-learning from a diverse set of tasks. Prior works on learning linear representations for meta-learning assume th…
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics
Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng +3
Why do biased predictions arise? What interventions can prevent them? We evaluate 8.2 million algorithmic predictions of math performance from 400 AI engineers, each of wh…
Is Private Learning Possible with Instance Encoding?
Nicholas Carlini, Samuel Deng, Sanjam Garg +6
A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy. In this work, we study whether a non-private learning algori…
Ensuring Fairness Beyond the Training Data
Debmalya Mandal, Samuel Deng, Suman Jana +2
We initiate the study of fair classifiers that are robust to perturbations in the training distribution. Despite recent progress, the literature on fairness has largely ignored the…
Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
Samuel Deng, Achille Varzi
In the ML fairness literature, there have been few investigations through the viewpoint of philosophy, a lens that encourages the critical evaluation of basic assumptions. The purp…