Pinned Repositories
ARM-gradient
Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
causal-rep-learning-reading-group
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.
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
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.
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).
mingzhang-yin's Repositories
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).
mingzhang-yin/ARM-gradient
Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
mingzhang-yin/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/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.
mingzhang-yin/Probabilistic-Best-Subset
A probabilistic solution to the exact best subset selection problem via continuous reformulation and gradient-based optimization.
mingzhang-yin/mingzhang-yin.github.io
Personal webpage
mingzhang-yin/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)
mingzhang-yin/causal-rep-learning-reading-group
mingzhang-yin/Conformal-sensitivity-analysis
Analyzing the sensitivity of an individual treatment effect over a potential violation of unconfoundedness.
mingzhang-yin/deconfounder_tutorial
mingzhang-yin/ARSM
Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder and reinforcement learning. ICML 2019
mingzhang-yin/best-subset
Comparisons between best subset selection and other popular estimators for sparse regression
mingzhang-yin/bootsens
Bootstrapping sensitivity analysis
mingzhang-yin/causal-text-papers
Curated research at the intersection of causal inference and natural language processing.
mingzhang-yin/Causalinference
Causal Inference in Python
mingzhang-yin/causality-for-ml
Causality for Machine Learning
mingzhang-yin/causalml
Uplift modeling and causal inference with machine learning algorithms
mingzhang-yin/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.
mingzhang-yin/empirical_calibration
mingzhang-yin/google-research
Google AI Research
mingzhang-yin/intro_bayesian_causal
Repository for Introduction to Bayesian Estimation of Causal Effects
mingzhang-yin/InvariantRiskMinimization
PyTorch code to run synthetic experiments.
mingzhang-yin/learning_to_adapt
Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning
mingzhang-yin/MacOSX-SDKs
A collection of those pesky SDK folders: MacOSX10.1.5.sdk thru MacOSX11.3.sdk
mingzhang-yin/MAR6669
course web
mingzhang-yin/Probabilistic-Conformal-Prediction-1
Probabilistic Conformal Prediction
mingzhang-yin/RaoBlackwellizedSGD
A public repository for our paper, Rao-Blackwellized Stochastic Gradients for Discrete Distributions
mingzhang-yin/reflective_survey
mingzhang-yin/shopper-src
Code for Shopper, a probabilistic model of shopping baskets
mingzhang-yin/SIVIR