gavin-s-smith

Clique

Python implementation of the CLIQUE subspace clustering algorithm.

deepar

Tensorflow implementation of Amazon DeepAR

dowhy

DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

EntropyRateEst

Entropy Rate Estimation

FastEMD_Python_Wrapper

Floor_ID

Code to perform full floor segmentation from images with partial (close range) depth informaion such as the Microsoft Kinect or the Asus Xtion Pro Live.

MathEquationsGoogleSlide

mcrforest

MobilityPredictabilityUpperBounds

Code for the paper "A Refined Limit on the Predictability of Human Mobility" in PERCOM 2014.

gavin-s-smith/MobilityPredictabilityUpperBounds

Code for the paper "A Refined Limit on the Predictability of Human Mobility" in PERCOM 2014.

gavin-s-smith/mcrforest

gavin-s-smith/Floor_ID

Code to perform full floor segmentation from images with partial (close range) depth informaion such as the Microsoft Kinect or the Asus Xtion Pro Live.

gavin-s-smith/Clique

Python implementation of the CLIQUE subspace clustering algorithm.

gavin-s-smith/deepar

Tensorflow implementation of Amazon DeepAR

gavin-s-smith/dowhy

DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

gavin-s-smith/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

gavin-s-smith/EntropyRateEst

Entropy Rate Estimation

gavin-s-smith/FastEMD_Python_Wrapper

gavin-s-smith/MathEquationsGoogleSlide

gavin-s-smith/nlabutils

gavin-s-smith/permimp

gavin-s-smith/pycforest

gavin-s-smith/random-forest-classifier

A random forest classifier written in python.

gavin-s-smith/random-forest-importances

Code to compute permutation and drop-column importances in Python scikit-learn random forests

gavin-s-smith/scikit-tensor-py3

Python library for multilinear algebra and tensor factorizations

gavin-s-smith/sfopy

Port of some functions from A. Krause. "SFO: A Toolbox for Submodular Function Optimization". Journal of Machine Learning Research (2010).

gavin-s-smith/svmmcr

gavin-s-smith/timeseries

Time series modelling and analysis

gavin-s-smith/TMA

Topic Model Analyzer

gavin-s-smith/vomm

Code for implementing variable order markov models in python.