Regression
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
The key idea of COPML is to securely encode the individual datasets to distribute the computation load effectively across many parties and to perform the training computations as well as the model updates in a distributed manner on the securely encoded data. We provide the privacy analysis of COPML and prove its convergence.
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis
Neural Information Processing Systems http://nips.cc/
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
In this work, we investigate an alternative setting for tuning regularization parameters, namely data-driven algorithm design, following the previous line of work by Balcan et al. [
A Appendix
To evaluate recovered data from Orthogonal Projection, we consider an additional architecture: ViT.
TRIAGE: Characterizing and auditing training data for improved regression
However, current data characterization methods are largely focused on classification settings, with regression settings largely understudied.
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Such assumptions allow for identification of causal target parameters. For instance, one may be interested in the average variance-weighted treatment effects (Robins et al., 2008; Li et al., 2011),