Statistical Learning
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
Note that lower values of ϵ and δ correspond to stronger privacy.
Neural Graph Generation from Graph Statistics
We describe a new setting for learning a deep graph generative model (GGM) from aggregate graph statistics, rather than from the graph adjacency matrix.
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication Martijn de V os
We discuss these works in more detail in Section 5. Authors are listed in alphabetical order.
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
However, SBC has drawbacks: The test statistic is somewhat ad-hoc, interactions are difficult to examine, multiple testing is a challenge, and the resulting p-value is not a divergence metric.
models (LMs). Given a fixed budget of tokens, we study how to best select data
A natural way to unlock particular capabilities is to improve this training data.
Differentially Private Image Classification by Learning Priors from Random Processes Xinyu T ang
We propose DP-RandP, a three-phase approach.
