mingzhang-yin

SIVI

A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural networks and hierarchical distribution (ICML 2018).

ARM-gradient

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

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)

CoCo

An optimization-based algorithm to accurately estimate the causal effects and robustly predict under distribution shifts. It leverages the invariance of causality over multiple environments.

Probabilistic-Best-Subset

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

mingzhang-yin.github.io

Personal webpage

Conformal-sensitivity-analysis

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

causal-rep-learning-reading-group

deconfounder_tutorial

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-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

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

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

SIVIR

VIPS

Code for Variational Inference with Pairwise Structure (VIPS) in "A Theoretical Case Study of Structured Variational Inference for Community Detection"