wentao-hu

wentao-hu's Stars

• hpcaitech/ColossalAI

  Making large AI models cheaper, faster and more accessible

  Language:Python38.3k3811.6k4.3k

• garrettj403/SciencePlots

  Matplotlib styles for scientific plotting

  Language:Python6.7k6091687

• interpretml/interpret

  Fit interpretable models. Explain blackbox machine learning.

  Language:C++6.1k144436720

• sksq96/pytorch-summary

  Model summary in PyTorch similar to `model.summary()` in Keras

  Language:Python4k37147412

• rguo12/awesome-causality-algorithms

  An index of algorithms for learning causality with data

  2.9k1089438

• secretflow/secretflow

  A unified framework for privacy-preserving data analysis and machine learning

  Language:Python2.3k53739364

• pytorch/opacus

  Training PyTorch models with differential privacy

  Language:Jupyter Notebook1.6k43285328

• Coder-Yu/QRec

  QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

  Language:Python1.6k69144404

• hidasib/GRU4Rec

  GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016 and its follow-up "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". The code is optimized for execution on the GPU.

  Language:Python7474045221

• jjbrophy47/machine_unlearning

  Existing Literature about Machine Unlearning

  71925490

• youngfish42/Awesome-Federated-Learning-on-Graph-and-Tabular-Data

  Federated learning on graph and tabular data related papers, frameworks, and datasets.

  Language:Python670134116

• xunzheng/notears

  DAGs with NO TEARS: Continuous Optimization for Structure Learning

  Language:Python5702217129

• THUwangcy/ReChorus

  “Chorus” of recommendation models: a light and flexible PyTorch framework for Top-K recommendation.

  Language:Python509144390

• guoyang9/NCF

  A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17

  Language:Python33242362

• sisinflab/elliot

  Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

  Language:Python270132049

• ChrisWaites/pyvacy

  Differentially Private Optimization for PyTorch 👁🙅‍♀️

  Language:Python1805830

• microsoft/robustdg

  Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

  Language:Python17110929

• cybertronai/autograd-hacks

  Language:Python15071331

• bargavj/EvaluatingDPML

  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.

  Language:Python12761050

• logseminar/Schedule

  Schedule for learning on graphs seminar

  1091105

• zyang1580/PDA

  This is an implementation for our SIGIR 2021 paper "Causal Intervention for Leveraging Popularity Bias inRecommendation" based on tensorflow..

  Language:Python9621729

• tsinghua-fib-lab/Causal-Recommender-Systems

  An index of causal inference based recommendation algorithms (TOIS).

  40103

• chenchongthu/Recommendation-Unlearning

  Language:Python35338

• RUCAIBox/Awesome-Privacy-Preserving-RS-Paper

  This repository collects the latest research progress of Privacy-Preserving Recommender Systems after 2018.

  27204

• zziqi/CTR-Estimation

  22106

• zyang1580/DCR

  This is an implementation for our paper "Addressing Confounding Feature Issue for Causal Recommendation" based on PyTorch.

  Language:Python16203

• rdz98/bprmf-pytorch

  推荐系统BPRMF模型的Pytorch实现，基于MovieLens数据集

  Language:Python10100

• pckennethma/NoLeaks-artifact

  [TIFS] The research artifact for NoLeaks algorithm

  Language:Python80

• JingsenZhang/CACF

  Code for CIKM 2021 short paper "Causally Attentive Collaborative Filtering."

  Language:Python7102

• sunblaze-ucb/Priv-PC-Differentially-Private-Causal-Graph-Discovery

  Language:Python7701