Machine Intelligence

CO-BED

September 2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED—a general, model-agnostic framework for designing contextual experiments using information-theoretic principles.

Machine Intelligence PyTorch Module Zoo

October 2021

A number of PyTorch modules (neural network components) that are broadly (re)usable. The goal of this release is to provide a library to machine learning researchers to use and advance the state-of-the-art in the area.

Project Azua: Data efficient decision making

September 2021

Many modern AI algorithms are known to be data-hungry, whereas human decision-making is much more efficient. The human can reason under uncertainty, actively acquire valuable information from the world to reduce uncertainty, and make personalized decisions given incomplete information. How…

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation [Dataset]

July 2021

Strategically Efficient RL

May 2021

This project implements exploration mechanisms for reinforcement learning that are specifically designed for non-cooperative multi-agent settings. Exploration mechanisms for single-agent RL (e.g. curiosity) are ill-suited to non-cooperative multi-agent settings, as they fail to account for the adversarial nature of these…

Microsoft TensorFlow2 GNN Library

February 2020

TensorFlow 2 library implementing Graph Neural Networks.

Pseudo-Task MAML

August 2018

This is PointSQL, the source codes of Natural Language to Structured Query Generation via Meta-Learning and Pointing Out SQL Queries From Text from Microsoft Research. We present the setup for the WikiSQL experiments.

Learning to Represent Programs with Graphs Dataset – ICLR 2018

April 2018

This download contains the graphs (parsed source code) for the open-source projects used in the ICLR 2018 paper “Learning to Represent Programs with Graphs”. In this work, we present how to construct graphs from source code and how to scale…