moprescu
CS PhD student at Cornell Tech. Former Senior Data and Applied Scientist at Microsoft Research.
PhD in CS @ Cornell TechNew York, NY
moprescu's Stars
microsoft/SynapseML
Simple and Distributed Machine Learning
py-why/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.
databricks/spark-deep-learning
Deep Learning Pipelines for Apache Spark
logangraham/arXausality
A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
Azure/cortana-intelligence-price-optimization
Retail industry solutions for product price optimization using the Cortana Intelligence Suite with end-to-end walkthrough
microsoft/subseasonal_data
Data access package for the SubseasonalClimateUSA dataset
Azure-Samples/hdinsight-pyspark-cntk-integration
Instructions and examples for installing CNTK on an HDInsight cluster and running CNTK-Pyspark applications from Jupyter notebooks.
CausalML/CDTE
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
CausalML/BLearner
Doubly-Valid/Doubly-Sharp Bounds for CATEs
deliarusu/discrimination-ML
Implementation of the ideas in the Equality of Opportunity in Supervised Learning paper