{"id":26436343,"url":"https://github.com/chenxingqiang/ml-density-ratio-estimation","last_synced_at":"2025-06-15T00:06:19.676Z","repository":{"id":95394575,"uuid":"326971322","full_name":"chenxingqiang/ML-Density-Ratio-Estimation","owner":"chenxingqiang","description":"ML-Density-Ratio-Estimation focusing on Applications, Fundamentals, OUTLOOK.","archived":false,"fork":false,"pushed_at":"2021-01-07T06:25:15.000Z","size":3719,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-15T00:06:18.362Z","etag":null,"topics":["ai","density","detection","estimation","machine-learning","python","ratio"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/chenxingqiang.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-01-05T10:59:39.000Z","updated_at":"2025-05-24T11:19:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"a8f01242-78a9-437d-9b63-44c230d5a489","html_url":"https://github.com/chenxingqiang/ML-Density-Ratio-Estimation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/chenxingqiang/ML-Density-Ratio-Estimation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chenxingqiang%2FML-Density-Ratio-Estimation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chenxingqiang%2FML-Density-Ratio-Estimation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chenxingqiang%2FML-Density-Ratio-Estimation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chenxingqiang%2FML-Density-Ratio-Estimation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chenxingqiang","download_url":"https://codeload.github.com/chenxingqiang/ML-Density-Ratio-Estimation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chenxingqiang%2FML-Density-Ratio-Estimation/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259901382,"owners_count":22929224,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","density","detection","estimation","machine-learning","python","ratio"],"created_at":"2025-03-18T08:16:20.193Z","updated_at":"2025-06-15T00:06:19.665Z","avatar_url":"https://github.com/chenxingqiang.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n# OUTLOOK\n\n- NAME： Density Ratio Estimation in Machine Learning\n\n- SOURCE： http://www.ms.k.u-tokyo.ac.jp/software.html\n\n- AIM： We aim at reproduce the density ratio estimation algorithms in this book: *Density Ratio Estimation in Machine Learning*\n\n\n\n\n\n## Fundamentals\n\n- Density ratio estimation\n  - KLIEP (Kullback-Leibler importance estimation procedure): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#KLIEP)\n  - GM-KLIEP (Gaussian-mixture KLIEP): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/gmkliep.html)\n  - LSIF (least-squares importance fitting): [R (by Takafumi Kanamori)](http://www.math.cm.is.nagoya-u.ac.jp/~kanamori/software/LSIF/)\n  - uLSIF (unconstrained LSIF): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#uLSIF), [R (by Takafumi Kanamori)](http://www.math.cm.is.nagoya-u.ac.jp/~kanamori/software/LSIF/), [C++ (by Issei Sato)](http://www.r.dl.itc.u-tokyo.ac.jp/~sato/uLSIF/)\n  - RuLSIF (relative uLSIF): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/RuLSIF.html), [R (by Max Wornowizki)](https://www.statistik.tu-dortmund.de/~wornowiz/RuLSIF.txt), [Python (by Song Liu)](http://www.ism.ac.jp/~liu/RuLSIF.html)\n- Density difference estimation\n  - LSDD (least-squares density difference): [MATLAB, Python (by Marthinus Christoffel du Plessis)](http://www.ms.k.u-tokyo.ac.jp/software.html#LSDD)\n- Density derivative estimation\n  - LSLDG (least-squares log-density gradient): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSLDG) (by Hiroaki Sasaki)\n- Mutual information estimation\n  - MLMI (maximum-likelihood mutual information): [MATLAB (with Taiji Suzuki)](http://www.ms.k.u-tokyo.ac.jp/software.html#MLMI)\n  - LSMI (least-squares mutual information): [MATLAB (with Taiji Suzuki)](http://www.ms.k.u-tokyo.ac.jp/software.html#LSMI)\n  - LSMI (multiplicative kernel model): [MATLAB (by Tomoya Sakai)](http://www.ms.k.u-tokyo.ac.jp/software.html#LSMI)\n  - LSQMI (least-squares quadratic mutual information): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSQMI)\n- Hetero-distributional subspace search\n  - LHSS (least-squares hetero-distributional search): [MATLAB (with Makoto Yamada)](http://www.ms.k.u-tokyo.ac.jp/software.html#LHSS)\n\n--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# Applications\n\n- Covariate shift adaptation\n  - IWLS+IWCV+uLSIF (importance-weighted least-squares + importance-weighted cross-validation + unconstrained least-squares importance fitting): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#IWLS)\n  - IWLR+KLIEP (importance-weighted logistic regression + Kullback-Leibler importance estimation procedure): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/iwklr.html)\n  - IWLSPC+IWCV+KLIEP (importance-weighted least-squares probabilistic classifier + importance-weighted cross-validation + Kullback-Leibler importance estimation procedure): [MATLAB (by Hirotaka Hachiya)](http://www.ms.k.u-tokyo.ac.jp/software.html#IWLSPC)\n- Class prior change adaptation\n  - uLSIF-based method: [MATLAB (by Marthinus Christoffel du Plessis)](http://sugiyama-www.cs.titech.ac.jp/~christo/pages/software-page.html)\n  - LSDD-based method: [MATLAB (by Marthinus Christoffel du Plessis)](http://sugiyama-www.cs.titech.ac.jp/~christo/pages/software-page.html)\n- Inlier-based outlier detection\n  - MLOD (maximum-likelihood outlier detection): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#MLOD)\n  - LSOD (least-squares outlier detection): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSOD)\n  - LSAD (least-squares anomaly detection): [Python (by John Quinn)](http://cit.mak.ac.ug/staff/jquinn/software/lsanomaly.html)\n- Feature selection\n  - MLFS (maximum-likelihood feature selection in supervised regression/classification): [MATLAB (with Taiji Suzuki)](http://www.ms.k.u-tokyo.ac.jp/software.html#MLFS)\n  - LSFS (least-squares feature selection in supervised regression/classification): [MATLAB (with Taiji Suzuki)](http://www.ms.k.u-tokyo.ac.jp/software.html#LSFS)\n  - L1-LSMI (L1-LSMI-based feature selection for supervised regression/classification): [MATLAB (by Wittawat Jitkrittum)](http://wittawat.com/pages/l1lsmi.html)\n  - HSIC-LASSO (Hilbert-Schmidt independence criterion + least absolute shrinkage and selection operator for high-dimensional feature selection in supervised regression/classification): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/hsiclasso.html)\n- Dimensionality reduction/feature extraction/metric learning\n  - NGCA (non-Gaussian component analysis, unsupervised linear dimensionality reduction): [MATLAB (by Gilles Blanchard)](http://users.math.uni-potsdam.de/~blanchard/software/NGCA_demo.tgz)\n  - LSDR (least-squares dimensionality reduction, supervised linear dimensionality reduction for regression/classification): [MATLAB (with Taiji Suzuki)](http://www.ms.k.u-tokyo.ac.jp/software.html#LSDR)\n  - SCA (sufficient component analysis, supervised linear dimensionality reduction for regression/classification): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/sca.html)\n  - LSQMID (least-squares quadratic mutual information derivative, supervised linear dimensionality reduction for regression/classification): [MATLAB (by Voot Tangkaratt)](http://www.ms.k.u-tokyo.ac.jp/software.html#LSQMID)\n  - LFDA (local Fisher discriminant analysis, supervised linear dimensionality reduction for classification): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LFDA)\n  - SELF (semi-supervised LFDA, semi-supervised linear dimensionality reduction for classification): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#SELF)\n  - LSCDA (least-squares canonical dependency analysis, linear dimensionality reduction for paired data): [MATLAB (by Masayuki Karasuyama)](http://www.bic.kyoto-u.ac.jp/pathway/krsym/software/LSCDA/index.html)\n  - SERAPH (semi-supervised metric learning paradigm with hyper-sparsity, semi-supervised metric learning for classification): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software/SERAPH.zip) (by Gang Niu)\n- Classification\n  - PU classification (learning from positive and unlabeled data): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#PU) (by Tomoya Sakai)\n  - PNU classification (semi-supervised learning based on PU classification): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#PNU) (by Tomoya Sakai)\n- Conditinonal probability estimation\n  - LSCDE (least-squares conditional density estimation): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSCDE)\n  - LSPC (least-squares probabilitic classifier): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSPC), [Python (by John Quinn)](http://cit.mak.ac.ug/staff/jquinn/software/lspc.html)\n  - SMIR (squared-loss mutual information regularization, semi-supervised probabilistic classification): [MATLAB (by Gang Niu and by Wittawat Jitkrittum)](http://www.ms.k.u-tokyo.ac.jp/software/SMIR.zip)\n- Independence test\n  - LSIT (least-squares independence test): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSIT)\n- Two-sample test\n  - LSTT (least-squares two-sample test): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSTT)\n- Change detection\n  - CD-RuLSIF (distributional change detection by RuLSIF): [MATLAB (by Song Liu)](http://www.ism.ac.jp/~liu/software.html)\n  - CD-KLIEP (structural change detection by sparse KLIEP): [MATLAB (by Song Liu)](http://www.ism.ac.jp/~liu/software.html)\n- Clustering\n  - SMIC (clustering based on maximization of squared-loss mutual information): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#SMIC)\n  - MVC (clustering based on maximization of volume): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software/MVC.zip) (by Gang Niu)\n  - LSLDG (clustering based on least-squares log-density gradient): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#LSLDG) (by Hiroaki Sasaki)\n- Independent component analysis\n  - LICA (independent component analysis): [MATLAB (by Taiji Suzuki)](http://www.is.titech.ac.jp/~s-taiji/software/LICA/index.html)\n- Causal direction inference\n  - LSIR (least-squares independence regression): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/lsir.html)\n- Cross-domain object matching\n  - LSOM (least-squares object matching): [MATLAB (by Makoto Yamada)](http://www.makotoyamada-ml.com/lsom.html)\n- Hidden Markov Model\n  - DRHMM (density-ratio hidden Markov model): [MATLAB and Python (by John Quinn)](http://cit.mak.ac.ug/staff/jquinn/software/densityratioHMM.html)\n- Sparse learning\n  - DAL (l1/grouped-l1/trace-norm regularization solver): [MATLAB (by Ryota Tomioka)](http://ttic.uchicago.edu/~ryotat/softwares/dal/)\n- Matrix/tensor factorization\n  - VBMF (variational Bayesian matrix factorization): [MATLAB](http://www.ms.k.u-tokyo.ac.jp/software.html#VBMF)\n  - Multitask learning with tensor factorization: [MATLAB (by Kishan Wimalawarne)](http://kishan-wimalawarne.com/software.html#nips2014)\n- Reinforcement learning\n  - IW-PGPE-OB (model-free policy gradient method with sample reuse): [MATLAB](https://sites.google.com/site/tingtingzhao1986phd/)\n- Crowdsourcing\n  - BBTA (bandit-based task assignment): [Python (by Hao Zhang)](https://github.com/justhao/bbta)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchenxingqiang%2Fml-density-ratio-estimation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchenxingqiang%2Fml-density-ratio-estimation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchenxingqiang%2Fml-density-ratio-estimation/lists"}