https://github.com/mbs0221-mlsys/Multitask-Learning

Awesome Multitask Learning Resources
https://github.com/mbs0221-mlsys/Multitask-Learning
convex-optimization deep-learning domain-adaptation federated-learning few-shot-learning machine-learning meta-learning multi-task-learning sparse-learning transfer-learning
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Awesome Multitask Learning Resources
Host: GitHub
URL: https://github.com/mbs0221-mlsys/Multitask-Learning
Owner: mbs0221-mlsys
Created: 2018-11-02T10:32:40.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2021-04-13T05:25:29.000Z (about 4 years ago)
Last Synced: 2024-12-26T16:03:09.948Z (4 months ago)
Topics: convex-optimization, deep-learning, domain-adaptation, federated-learning, few-shot-learning, machine-learning, meta-learning, multi-task-learning, sparse-learning, transfer-learning
Homepage:
Size: 29.3 MB
Stars: 652
Watchers: 28
Forks: 144
Open Issues: 2
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome-machine-learning-resources - **[List - Learning?style=social) (Table of Contents)
README

        # Multitask-Learning

   多任务学习相关资料，主要包括代表性学者主页、论文、综述、幻灯片、论文集和开源代码。欢迎分享~ 

   

   This repository collects Multitask-Learning related materials, mainly including the homepage of representative scholars, papers, surveys, slides, proceedings, and open-source projects. Welcome to share these materials!

* Something New!!!

    * [CS330: Deep Multi-Task and Meta-Learning](http://cs330.stanford.edu/)

* Homepage

    * [Rich Caruana's (terribly out-of-date) Home Page](http://www.cs.cmu.edu/~caruana/)

    * [Massimiliano Pontil - UCL](http://www0.cs.ucl.ac.uk/staff/M.Pontil/)

    * [Jiayu Zhou | Machine Learning at Michigan State University](http://jiayuzhou.github.io/)

    * [Yu Zhang (张宇) - HKUST](https://www.cse.ust.hk/~yuzhangcse/)

    * [Tong Zhang (张潼)- Tencent AI Lab](http://tongzhang-ml.org/publication.html)

    * [Fuzhen Zhuang (庄福振)](http://www.intsci.ac.cn/users/zhuangfuzhen/#Resources)

    * [Lei Han's Homepage](http://sysbio.cvm.msstate.edu/~leihan/)

    * [Pattern Recognition & Neural Computing Group - NUAA](http://parnec.nuaa.edu.cn/)

    * [Fei Sha University of Southern California](http://www-bcf.usc.edu/~feisha/index.html)

    * [Dr Xiaojun Chang – Faculty of Information Technology – Monash University](http://www.cs.cmu.edu/~uqxchan1/index.html)

    * [Yu-Gang Jiang Prof.](http://www.yugangjiang.info/)

    * [Zhuoliang Kang Ph.D student, Computer Science, USC](http://zhuoliang.me/research.html)

    * [Shiliang Sun’s Home Page](http://www.cs.ecnu.edu.cn/~slsun/)

    * [Dr. Timothy Hospedales](http://www.eecs.qmul.ac.uk/~tmh/index.html#home)

    * [ML^2 @ UCF](http://ml.cecs.ucf.edu/node/52)

    * [Elisa Ricci](https://sites.google.com/site/elisaricciunipg/home)

    * [Gjorgji Strezoski](https://staff.fnwi.uva.nl/g.strezoski/)

    * [Machine Learning with Interdependent and Non-identically Distributed Data](https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=15152)

    * [SFU Machine Learning Reading Group](https://www.cs.ubc.ca/~schmidtm/MLRG/)

* Github

    * Logistic Regression

        * [Multi-task logistic regression in brain-computer interfaces](https://github.com/vinay-jayaram/MTlearning)

    * Bayesian Methods

        * [Kernelized Bayesian Multitask Learning](https://github.com/mehmetgonen/kbmtl)

        * [Parametric Bayesian multi-task learning for modeling biomarker trajectories](https://github.com/LeonAksman/bayes-mtl-traj)

        * [Bayesian Multitask Multiple Kernel Learning](https://github.com/mehmetgonen/bmtmkl)

    * Gaussian Process

        * [Multi-task Gaussian process (MTGP)](https://github.com/ebonilla/mtgp)

        * [Gaussian process multi-task learning](https://github.com/amarquand/gpmtl)

    * Sparse & Low Rank Methods

        * [Asymmetric Multi-Task Learning](https://github.com/BlasterL/AMTL)

        * [Hierarchical_Multi_Task_Learning](https://github.com/digbose92/Hierarchical_Multi_Task_Learning)

        * [Asynchronous Multi-Task Learning](https://github.com/illidanlab/AMTL)

        * [HMTL: Hierarchical Multi-Task Learning](https://github.com/huggingface/hmtl)

        * [Multi-task feature learning](https://github.com/argyriou/multi_task_learning)

        * [Multiplicative MultiTask Feature Learning (MMTFL)](https://github.com/JunYongJeong/MMTFL)

        * [Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks](https://github.com/AlamiMejjati/Mtl-Implem)

        * [Variable Selection and Task Grouping for Multi-Task Learning (VSTG-MTL)](https://github.com/JunYongJeong/VSTG-MTL)

        * [Calibrated Multi-Task Learning Based on Non-convex Low Rank](https://github.com/sudalvxin/Multi-task-Learning)

        * [Multi-task_Survival_Analysis](https://github.com/yanlirock/Multi-task_Survival_Analysis)

        * [A Multi-Task Learning Formulation for Survival Analysis](https://github.com/yanlirock/MTLSA)

    * [Federated Multi-Task Learning](https://github.com/gingsmith/fmtl)

    * Online Learning

        * [Online Multi-Task Learning Toolkit (OMT) v1.0](https://github.com/lancopku/Multi-Task-Learning)

    * Reinforcement Learning

        * [Robust-Multitask-Reinforcement Learning](https://github.com/Alfo5123/Robust-Multitask-RL)

    * Package & Toolbox

        * [MALSAR: Multi-task learning via Structural Regularization](http://jiayuzhou.github.io/MALSAR/)

        * [MALSAR package Version 1.1](https://github.com/xiayan/MTL)

        * [Multi Task Learning Package for Matlab](https://github.com/cciliber/matMTL)

        * [Regularized Multi-task Learning in R](https://github.com/transbioZI/RMTL)

        * [Package to apply MTL on a few dataset](https://github.com/chcorbi/MultiTaskLearning)

        * [Matlab MultiClass MultiTask Learning (MCMTL) toolbox](https://github.com/dsmbgu8/MCMTL)

        * [Personalized Multitask Learning](https://github.com/mitmedialab/PersonalizedMultitaskLearning)

* Papers

    * [Multitask Learning](https://link.springer.com/article/10.1023/A:1007379606734)

    * [An overview of multi-task learning](https://academic.oup.com/nsr/article/5/1/30/4101432)

    * [A brief review on multi-task learning](https://link.springer.com/article/10.1007%2Fs11042-018-6463-x)

    * [A review on multi-task metric learning](https://bdataanalytics.biomedcentral.com/articles/10.1186/s41044-018-0029-9)

    * [Safe sample screening for regularized multi-task learning](https://linkinghub.elsevier.com/retrieve/pii/S0950705120304469)

    * [Multi-task feature learning by using trace norm regularization](http://adsabs.harvard.edu/abs/2017OPhy...15...79J)

    * [Multi-Task Clustering with Model Relation Learning](https://doi.org/10.24963/ijcai.2018/435)

    * [Online Multitask Learning](https://www.microsoft.com/en-us/research/publication/online-multitask-learning/)

    * [Online multitask relative similarity learning](https://ink.library.smu.edu.sg/sis_research/3846/)

    * [New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks](https://sites.google.com/site/learningacross/home/accepted-papers)

    * [Manifold Regularized Multi-Task Learning](https://doi.org/10.1007/978-3-642-34487-9_64)

    * [Multi-Objective Multi-Task Learning](./pdf/Multi-Objective%20Multi-Task%20Learning.pdf)

    * [Multi-Task Active Learning](./pdf/Multi-Task%20Active%20Learning.pdf)

* Arxiv

    * [A Survey on Multi-Task Learning](https://arxiv.org/abs/1707.08114)

    * [Self-Paced Multi-Task Clustering](https://arxiv.org/abs/1808.08068)

    * [Model-Protected Multi-Task Learning](https://arxiv.org/abs/1809.06546)

* Slides

    * Article slides

        * [Multi-Task Feature Learning](./pdf/oh06_argyriou_mtfl_01.pdf)

        * [Multi-Stage Multi-Task Feature Learning](./pdf/machine_zhang_learning_01.pdf)

        * [A Dirty Model for Multitask Learning](./pdf/nips2010_jalali_dmm_01.pdf)

        * [Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks](./pdf/kdd2010_chen_lislrpmt_01.ppt)

        * [A Graph-based Framework for Multi-Task Multi-View Learning](./pdf/A%20Graph-based%20Framework%20for%20Multi-Task%20Multi-View%20Learning.pdf)

    * Lecture slides

        * [Multi-Task Learning: The Bayesian Way, 2006](./pdf/oh06_heskes_bw_01.pdf)

        * [Multi-task Gaussian Process Prediction, 2008](./pdf/bark08_williams_mtlwgp_01.pdf)

        * [Multitask Multiple Kernel Learning, 2010](./pdf/nipsworkshops2010_widmer_mmk_01.pdf)

        * [Multi-task Regularization of Generative Similarity Models, 2011](./pdf/simbad2011_cazzanti_generative_01.pdf)

        * [Learning Multiple Tasks with a Sparse Matrix-Normal Penalty, 2011](./pdf/Esther3.25.2011.pdf)

        * [Multi-Task Learning via Matrix Regularization](./pdf/lkasok08_argyriou_mtlvmr_01.pdf)

        * [Multi-Task Learning: Theory, Algorithms, and Applications, 2012](./pdf/Multi-Task%20Learning-Theory,%20Algorithms,%20and%20Applications.pdf)

        * [Multi-task and Transfer Learning, 2013](./pdf/Multi-task%20and%20Transfer%20Learning.pdf)

        * [Multi-task Learning and Structured Sparsity, 2013](./pdf/Multi-task%20Learning%20and%20Structured%20Sparsity.pdf)

        * [Multi-task Learning, 2013](./pdf/roks2013_pontil_learning_01.pdf)

        * [Multi-task Learning, 2014](./pdf/Multi-task%20Learning-1.pdf)

        * [Multi-Task Learning: Models, Optimization and Applications, 2016](./pdf/Multi-Task%20Learning-Models,%20Optimization%20and%20Applications.pdf)

        * [Domain Adaptation / Multi-Task Learning Methods and Examples](./pdf/Domain%20Adaptation%20–%20Algorithms,%20Variants%20and%20Extensions.pdf)

        * [Recent Advances in Multi-Task Learning](http://jiayuzhou.github.io/talks/advances_in_multitask_learning.pdf)

* Resources

    * [Task Sensitive Feature Exploration and Learning for Multi-Task Graph Classification](http://www.cse.fau.edu/~xqzhu/FelMuG/index.html)

    * [BMTMKL: Bayesian Multitask Multiple Kernel Learning](https://research.cs.aalto.fi/pml/software/bmtmkl/)

    * [Multitask Learning / Domain Adaptation](http://www.cs.cornell.edu/~kilian/research/multitasklearning/multitasklearning.html)

    * [Multitask Kernel Methods](./docs/mkl.md)

    * [Multitask Deep Learning](./docs/mdl.md)

* Package & Toolbox

    * [RMTL: Regularized Multi-Task Learning

](https://cran.r-project.org/web/packages/RMTL/index.html)

    * [Multi-Task Learning: Theory, Algorithms, and Applications](https://archive.siam.org/meetings/sdm12/multi.php)

    * [An Tutorial for Regularized Multi-task Learning using the package RMTL](https://cran.r-project.org/web/packages/RMTL/vignettes/rmtl.html)    

    * [SparseMTL Toolbox](http://asi.insa-rouen.fr/enseignants/~arakoto/code/SparseMTL.html#description)

    * [Probabilistic Machine Learning](https://research.cs.aalto.fi/pml/software.shtml)

## Related Research Areas

* Multi-Label Learning

   * [KEEL-dataset repository](https://sci2s.ugr.es/keel/multilabel.php#sub10)

* Few-Shot Learning & Meta-Learning

    * [PyTorch Meta-learning Framework for Researchers](https://github.com/learnables/learn2learn)

    * [Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning](https://github.com/floodsung/Meta-Learning-Papers)

    * [Awesome Meta Learning](https://github.com/sudharsan13296/Awesome-Meta-Learning)

    * [awesome-meta-learning](https://github.com/dragen1860/awesome-meta-learning)

    * [Torchmeta](https://github.com/tristandeleu/pytorch-meta)

    * [Model-Agnostic Meta-Learning](https://github.com/dragen1860/MAML-Pytorch)

* Federated Learning

    * [Federated Learning](https://github.com/ZeroWangZY/federated-learning)

    * [Awesome Federated Learning](https://github.com/poga/awesome-federated-learning)

    * [Awesome Federated Computing](https://github.com/tushar-semwal/awesome-federated-computing)

## Convex Optimization

* [EE364a: Convex Optimization I Professor Stephen Boyd, Stanford University](http://web.stanford.edu/class/ee364a/)

* [EE227BT: Convex Optimization  —  Fall 2013](https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/index.html)

* [CRAN - Package ADMM](http://cran.stat.ucla.edu/web/packages/ADMM/)

* [Proximal-Proximal-Gradient Method](https://www.math.ucla.edu/~wotaoyin/papers/prox_prox_grad.html)

* [Proximal gradient method](http://bicmr.pku.edu.cn/~wenzw/opt2015/lect-proxg.pdf)
ecosyste.ms

Data

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https://github.com/mbs0221-mlsys/Multitask-Learning

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README
