【Awesome】最全的机器学习可解释性资料(machine-learning-interpretability)

3 月 1 日 专知

【导读】 大家知道机器学习和深度学习在近几年已经取得了极大的发展,应用范围十分广泛,对各行各业也都产生了深远的影响。但是现在从事机器学习和深度学习的研究人员研究的焦点逐渐变成可解释性。什么是可解释性?可解释性就是不仅仅要知道怎么做,同时也要知道为什么这么做会产生这样的结果,不论结果是否理想。今天小编就给大家分享一些机器学习可解释性资料


下面是一个局部的机器学习蓝图,可以从总体上帮助降低做机器学习任务的风险程度。



▌资料目录




  • 全面的软件示例和教程(Comprehensive Software Examples and Tutorials)

  • 可解释性或合适的增强软件包Explainability- or Fairness-Enhancing Software Package

    • Browser

    • Python

    • R

  • 免费的书(Free Books)

  • 其他可解释性和合适的资源和列表

  • 论文(Review and General Papers)

  • 教学资源(Teaching Resources)

  • 可解释(“白盒”)或合适的建模包(Interpretable ("Whitebox") or Fair Modeling Packages)

    • C/C++

    • Python

    • R

Comprehensive Software Examples and Tutorials




  • Getting a Window into your Black Box Model

  • IML

  • Interpretable Machine Learning with Python

  • Interpreting Machine Learning Models with the iml Package

  • Machine Learning Explainability by Kaggle Learn

  • Model Interpretability with DALEX

  • Model Interpretation series by Dipanjan (DJ) Sarkar:

    • The Importance of Human Interpretable Machine Learning

    • Model Interpretation Strategies

    • Hands-on Machine Learning Model Interpretation

  • Partial Dependence Plots in R

  • Visualizing ML Models with LIME


Expalinability-or Fairness-Enhancing Software Packages




  • Browser

    • What-if Tool

  • Python

    • ‍‍aequitas

    • AI Fairness 360

    • anchor

    • casme

    • cleverhans

    • ContrastiveExplanation (Foil Trees)

    • deeplift

    • deepvis

    • eli5

    • fairml

    • fairness

    • Integrated-Gradients

    • lofo-importance

    • L2X

    • lime

    • PDPbox

    • pyBreakDown

    • PyCEbox

    • shap

    • Skater

    • rationale

    • tensorflow/lucid

    • tensorflow/model-analysis

    • Themis

    • themis-ml

    • treeinterpreter

    • woe

    • xai

  • R

    • ALEPlot

    • breakDown

    • DALEX

    • ExplainPrediction

    • featureImportance

    • forestmodel

    • fscaret

    • ICEbox

    • iml

    • lightgbmExplainer

    • lime

    • live

    • mcr

    • pdp

    • shapleyR

    • smbinning

    • vip

    • xgboostExplainer


▌Free Books




  • Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models

  • Fairness and Machine Learning

  • Interpretable Machine Learning


▌Other Interpretability and Fairness Resources and Lists



  • 8 Principles of Responsible ML

  • An Introduction to Machine Learning Interpretability

  • Awesome interpretable machine learning ;)

  • Awesome machine learning operations

  • algoaware

  • criticalML

  • Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship

  • Machine Learning Ethics References

  • Machine Learning Interpretability Resources

  • MIT AI Ethics Reading Group

  • XAI Resources


▌Review and General Papers




  • A Comparative Study of Fairness-Enhancing Interventions in Machine Learning

  • A Survey Of Methods For Explaining Black Box Models

  • Challenges for Transparency

  • Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

  • On the Art and Science of Machine Learning Explanations

  • On the Responsibility of Technologists: A Prologue and Primer

  • Please Stop Explaining Black Box Models for High-Stakes Decisions

  • The Mythos of Model Interpretability

  • The Promise and Peril of Human Evaluation for Model Interpretability

  • Towards A Rigorous Science of Interpretable Machine Learning

  • Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda


▌Review and General Papers




  • An Introduction to Data Ethics

  • Fairness in Machine Learning

  • Human-Center Machine Learning

  • Practical Model Interpretability


▌Interpretable("Whitebox") or Fair Modeing Packages




  • C/C++

    • Certifiably Optimal RulE ListS

  • Python

    • Bayesian Case Model

    • Bayesian Ors-Of-Ands

    • Bayesian Rule List (BRL)

    • fair-classification

    • Falling Rule List (FRL)

    • H2O-3

      • Penalized Generalized Linear Models

      • Sparse Principal Components (GLRM)

    • Monotonic XGBoost

    • pyGAM

    • Risk-SLIM

    • Scikit-learn

      • Decision Trees

      • Generalized Linear Models

      • Sparse Principal Components

    • sklearn-expertsys

    • skope-rules

    • Super-sparse Linear Integer models (SLIMs)

  • R

    • arules

    • Causal SVM

    • elasticnet

    • gam

    • glmnet

    • H2O-3

      • Penalized Generalized Linear Models

      • Sparse Principal Components (GLRM)

    • Monotonic XGBoost

    • quantreg

    • rpart

    • RuleFit

    • Scalable Bayesian Rule Lists (SBRL)



参考链接:https://github.com/jphall663/awesome-machine-learning-interpretability#comprehensive-software-examples-and-tutorials

https://github.com/h2oai/mli-resources



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We study post-Lie algebra structures on pairs of Lie algebras $(\mathfrak{g},\mathfrak{n})$, motivated by nil-affine actions of Lie groups. We prove existence results for such structures depending on the interplay of the algebraic structures of $\mathfrak{g}$ and $\mathfrak{n}$. We consider the classes of simple, semisimple, reductive, perfect, solvable, nilpotent, abelian and unimodular Lie algebras. Furthermore we consider commutative post-Lie algebra structures on perfect Lie algebras. Using Lie algebra cohomology we prove that such structures are trivial in several cases. We classify commutative structures on low-dimensional Lie algebras, and study the case of nilpotent Lie algebras.

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