euzmin

euzmin's Stars

• maks-sh/scikit-uplift

  :exclamation: uplift modeling in scikit-learn style in python :snake:

  Language:Python73198

• dgliu/KDD23_EFIN

  Experiments codes for SIGKDD '23 paper "Explicit Feature Interaction-aware Uplift Network for Online Marketing"

  Language:Python397

• aifor/eeuen

  code for "Addressing Exposure Bias in Uplift Modeling forLarge-scale Online Advertising"

  Language:Python237

• shenweichen/DeepCTR

  Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

  Language:Python7.6k2.2k

• zhougr1993/DeepInterestNetwork

  Language:Python1.6k558

• www2022paper/WWW-2022-PAPER-SUPPLEMENTARY-MATERIALS

  Language:Jupyter Notebook3319

• MasaAsami/introduction_to_CFR

  CFR

  Language:Jupyter Notebook91

• st-tech/zr-obp

  Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation

  Language:Python64387

• farazmah/dragonnet-pytorch

  pytorch implementation of dragonnet

  Language:Python273

• zhihaiLLM/wisdomInterrogatory

  Language:Python48942

• dougbrion/pytorch-classification-uncertainty

  This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

  Language:Python43562

• CrystalXuR/ECETH

  Language:R4

• tloen/alpaca-lora

  Instruct-tune LLaMA on consumer hardware

  Language:Jupyter Notebook18.6k2.2k

• QSCTech/zjunet

  Command Line Scripts for ZJU (VPN / WLAN / DNS)

  Language:Shell30248

• CausalTeam/ADMIT

  Language:Python72

• jsyoon0823/GANITE

  Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018

  Language:Python5316

• gregversteeg/NPEET

  Non-parametric Entropy Estimation Toolbox

  Language:Python37589

• kailiang-zhong/DESCN

  Implementation of paper DESCN, which is accepted in SIGKDD 2022.

  Language:Jupyter Notebook6313

• zhenxingjian/Partial_Distance_Correlation

  This is the official GitHub for paper: On the Versatile Uses of Partial Distance Correlation in Deep Learning, in ECCV 2022

  Language:Jupyter Notebook17216

• vanderschaarlab/mlforhealthlabpub

  Machine Learning and Artificial Intelligence for Medicine.

  Language:Python426170

• py-why/causal-learn

  Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

  Language:Python1.2k191

• causal-machine-learning-lab/mliv

  Language:Python264

• karpathy/pytorch-made

  MADE (Masked Autoencoder Density Estimation) implementation in PyTorch

  Language:Python53885

• shantanu-ai/DPN-SA

  Repository of Deep Propensity Network - Sparse Autoencoder(DPN-SA) to calculate propensity score using sparse autoencoder

  Language:Python102

• wetliu/energy_ood

  Language:Python39865

• bvegetabile/entbal

  An Alternative Implementation of Entropy Balancing Weights For Estimating Causal Effects

  Language:R10

• 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.

  Language:Jupyter Notebook3.8k714

• uber/causalml

  Uplift modeling and causal inference with machine learning algorithms

  Language:Python5.1k779

• facebookresearch/barlowtwins

  PyTorch implementation of Barlow Twins.

  Language:Python967128

• jaredhuling/independenceWeights

  Construction of weights for causal inference for continuous treatments

  Language:R6