This repository contains the source code for running a privacy preserving federated learning simulator. The source code is currently set up for the configuration of three clients performing secure and differentially private federated learning using logistic regression on the MNIST dataset. 

A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.

This repository contains code for reproducing the results of Combining Diverse Feature Priors

Counterfactual regression (CFR) by learning balanced representations, as developed by Johansson, Shalit & Sontag (2016) and Shalit, Johansson & Sontag (2016). cfrnet is implemented in Python using TensorFlow 0.12.0-rc1 and NumPy 1.11.3. The code has not been tested with TensorFlow 1.0.

Drake (“dragon” in Middle English) is a C++ toolbox started by the Robot Locomotion Group at the MIT Computer Science and Artificial Intelligence Lab (CSAIL). The development team has now grown significantly, with core development led by the Toyota Research Institute. It is a collection of tools for analyzing the dynamics of our robots and building control systems for them, with a heavy emphasis on optimization-based design/analysis.

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems and automatically guides the design of experiments to be evaluated.

Instruction THroughput Estimator using MAchine Learning (ITHEMAL)

Improving Deep Network Debuggability via Sparse Decision Layers 

Minimal, standalone library for solving GLMs in PyTorch

Measuring Generalization with Optimal Transport