Pinned Repositories
MathProblem-Dataset
Math Problem Data Set
curriculum-tsp
Curriculum Learning for Combinatorial Optimization
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.
examples
Learn Micro by examples
Graph2Tree
Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)
Minimum-Entropy-Coupling-AISTATS-2023
Supplementary code for "Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier" by Spencer Compton, Dmitriy Katz, Benjamin Qi, Kristjan Greenewald, Murat Kocaoglu. https://proceedings.mlr.press/v206/compton23a.html
SpencerCompton's Repositories
SpencerCompton/Minimum-Entropy-Coupling-AISTATS-2023
Supplementary code for "Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier" by Spencer Compton, Dmitriy Katz, Benjamin Qi, Kristjan Greenewald, Murat Kocaoglu. https://proceedings.mlr.press/v206/compton23a.html
SpencerCompton/curriculum-tsp
Curriculum Learning for Combinatorial Optimization
SpencerCompton/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.
SpencerCompton/examples
Learn Micro by examples
SpencerCompton/Graph2Tree
Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)