Search Results for author:
Found 4 papers, 2 papers with code
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design
Specifically, they rely on the discreteness of the loss function, which means that real-valued weights cannot be directly used.
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores.
Certified Robustness to Adversarial Examples with Differential Privacy
Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth.
