mingzhang-yin Goto Github PK

arm-gradient

Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)

arsm

Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder and reinforcement learning. ICML 2019

best-subset

Comparisons between best subset selection and other popular estimators for sparse regression

bootsens

Bootstrapping sensitivity analysis

causal-rep-learning-reading-group

causal-text-papers

Curated research at the intersection of causal inference and natural language processing.

causalinference

Causal Inference in Python

causality-for-ml

Causality for Machine Learning

causalml

Uplift modeling and causal inference with machine learning algorithms

coco

An optimization algorithm to estimate the causal effects and produce distributionally robust prediction. It leverages the invariance property of causal mechanism over multiple environments.

conformal-sensitivity-analysis

Analyzing the sensitivity of an individual treatment effect over a potential violation of unconfoundedness.

convergence-of-gradient-em-on-multi-component-mixture-of-gaussians

Proof of local convergence region and convergence rate of gradient EM on general GMM, for both population and finite sample setting. (NeurIPS 2017)

deconfounder_tutorial

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.

empirical_calibration

google-research

Google AI Research

intro_bayesian_causal

Repository for Introduction to Bayesian Estimation of Causal Effects

invariantriskminimization

PyTorch code to run synthetic experiments.

learning_to_adapt

Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning

macosx-sdks

A collection of those pesky SDK folders: MacOSX10.1.5.sdk thru MacOSX11.3.sdk

mar6669

course web

meta-learning-without-memorization

A study on the following problems: what the memorization problem is in meta-learning; why memorization problem happens; and how we can prevent it. (ICLR 2020)

mingzhang-yin.github.io

Personal webpage

probabilistic-best-subset

A probabilistic solution to the exact best subset selection problem via continuous reformulation and gradient-based optimization.

probabilistic-conformal-prediction-1

Probabilistic Conformal Prediction

raoblackwellizedsgd

A public repository for our paper, Rao-Blackwellized Stochastic Gradients for Discrete Distributions

reflective_survey

shopper-src

Code for Shopper, a probabilistic model of shopping baskets

sivi

Using neural networks to build an expressive hierarchical distribution; A variational inference method to accurately estimate the posterior uncertainty; A fast and general method for approximate Bayesian inference. (ICML 2018)

sivir