Nancy’s images of harmony
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Nancy is collecting images and stories from people about what the concept of 'harmony' means to them. People are invited to email Nancy their submissions to an email address provided. The goal is to gather different perspectives on harmony from around the world.
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Caldecott illustration analysis
This is my Caldecott Illustration Analysis over the book Madeline's Rescue by Ludwig Bemelmans.
Fairness-aware Classifier with Prejudice Remover Regularizer
Fairness-aware Classifier with Prejudice Remover Regularizer
Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD), Part II, pp.35-50 (2012)
Article @ Official Site: http://dx.doi.org/10.1007/978-3-642-33486-3_3
Article @ Personal Site: http://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf
Handnote: http://www.kamishima.net/archive/2012-p-ecmlpkdd-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Conference Homepage: http://www.ecmlpkdd2012.net/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
The Independence of Fairness-aware Classifiers
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IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
Enhancement of the Neutrality in Recommendation
Enhancement of the Neutrality in Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2012
Article @ Official Site: http://ceur-ws.org/Vol-893/
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2012-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop2012
Abstract:
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To provide such a recommendation, we assume that a user specifies a viewpoint toward which the user want to enforce the neutrality, because recommendation that is neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information neutral recommendation algorithm by introducing a penalty term to enforce the statistical independence between the target viewpoint and a preference score. We empirically show that our algorithm enhances the independence toward the specified viewpoint by and then demonstrate how sets of recommended items are changed.
Efficiency Improvement of Neutrality-Enhanced Recommendation
Efficiency Improvement of Neutrality-Enhanced Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2013
Article @ Official Site: http://ceur-ws.org/Vol-1050/
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs/
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop/
Abstract:
This paper proposes an algorithm for making recommendations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoiding decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recommendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we implement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical independence between the target viewpoint and a rating. We empirically show that our algorithm enhances the independence from the specified viewpoint.
Consideration on Fairness-aware Data Mining
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Invited Talk @ Workshop on Fairness, Accountability, and Transparency in Machine Learning
In conjunction with the ICML 2015 @ Lille, France, Jul. 11, 2015
Web Site: http://www.kamishima.net/fadm/
Handnote: http://www.kamishima.net/archive/2015-ws-icml-HN.pdf
The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:
1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.
2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.
3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.
Joint work with Jun Sakuma, Shotaro Akaho, and Hideki Asoh
Pythonによる機械学習実験の管理
Python をつかったアルゴリズムの実験で，実験条件の設定や，その記録など実験管理，epydoc を用いたソフトの文書化，そして gxp を使ったクラスタ環境下での実行について簡単に紹介します．
Tokyo.Scipy #1 http://partake.in/events/3ab83ec0-e987-4956-b726-faf7777a2c83
WSDM2016勉強会 資料
WSDM2016勉強会 https://atnd.org/events/74341
論文：Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
著者：F. Kooti and K. Lerman and L. M. Aiello and M. Grbovic and N. Djuric
論文リンク： http://dx.doi.org/10.1145/2835776.2835831
OpenOpt の線形計画で圧縮センシング
圧縮センシングの実装を具体例に，最適化エンジンのpython用フロントエンドである OpenOpt と，その拡張パッケージである FuncDesigner の使い方を紹介します．
Tokyo.Scipy #3 http://partake.in/events/ac0fcc7d-a289-4e2a-bb8e-1965aab8b17b
Sample Code : https://github.com/tokyo-scipy/archive/tree/master/003/shima__shima
KDD2016勉強会 資料
KDD2016勉強会 https://atnd.org/events/80771
論文：“Why Should I Trust You?”Explaining the Predictions of Any Classifier
著者：M. T. Ribeiro and S. Singh and C. Guestrin
論文リンク： http://www.kdd.org/kdd2016/subtopic/view/why-should-i-trust-you-explaining-the-predictions-of-any-classifier
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Caldecott illustration analysis
This is my Caldecott Illustration Analysis over the book Madeline's Rescue by Ludwig Bemelmans.
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Fairness-aware Classifier with Prejudice Remover Regularizer
Fairness-aware Classifier with Prejudice Remover Regularizer
Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD), Part II, pp.35-50 (2012)
Article @ Official Site: http://dx.doi.org/10.1007/978-3-642-33486-3_3
Article @ Personal Site: http://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf
Handnote: http://www.kamishima.net/archive/2012-p-ecmlpkdd-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Conference Homepage: http://www.ecmlpkdd2012.net/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
The Independence of Fairness-aware Classifiers
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
Enhancement of the Neutrality in Recommendation
Enhancement of the Neutrality in Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2012
Article @ Official Site: http://ceur-ws.org/Vol-893/
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2012-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop2012
Abstract:
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To provide such a recommendation, we assume that a user specifies a viewpoint toward which the user want to enforce the neutrality, because recommendation that is neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information neutral recommendation algorithm by introducing a penalty term to enforce the statistical independence between the target viewpoint and a preference score. We empirically show that our algorithm enhances the independence toward the specified viewpoint by and then demonstrate how sets of recommended items are changed.
Efficiency Improvement of Neutrality-Enhanced Recommendation
Efficiency Improvement of Neutrality-Enhanced Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2013
Article @ Official Site: http://ceur-ws.org/Vol-1050/
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs/
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop/
Abstract:
This paper proposes an algorithm for making recommendations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoiding decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recommendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we implement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical independence between the target viewpoint and a rating. We empirically show that our algorithm enhances the independence from the specified viewpoint.
Consideration on Fairness-aware Data Mining
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Correcting Popularity Bias by Enhancing Recommendation Neutrality
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Invited Talk @ Workshop on Fairness, Accountability, and Transparency in Machine Learning
In conjunction with the ICML 2015 @ Lille, France, Jul. 11, 2015
Web Site: http://www.kamishima.net/fadm/
Handnote: http://www.kamishima.net/archive/2015-ws-icml-HN.pdf
The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:
1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.
2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.
3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.
Joint work with Jun Sakuma, Shotaro Akaho, and Hideki Asoh
Pythonによる機械学習実験の管理
Python をつかったアルゴリズムの実験で，実験条件の設定や，その記録など実験管理，epydoc を用いたソフトの文書化，そして gxp を使ったクラスタ環境下での実行について簡単に紹介します．
Tokyo.Scipy #1 http://partake.in/events/3ab83ec0-e987-4956-b726-faf7777a2c83
WSDM2016勉強会 資料
WSDM2016勉強会 https://atnd.org/events/74341
論文：Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
著者：F. Kooti and K. Lerman and L. M. Aiello and M. Grbovic and N. Djuric
論文リンク： http://dx.doi.org/10.1145/2835776.2835831
OpenOpt の線形計画で圧縮センシング
圧縮センシングの実装を具体例に，最適化エンジンのpython用フロントエンドである OpenOpt と，その拡張パッケージである FuncDesigner の使い方を紹介します．
Tokyo.Scipy #3 http://partake.in/events/ac0fcc7d-a289-4e2a-bb8e-1965aab8b17b
Sample Code : https://github.com/tokyo-scipy/archive/tree/master/003/shima__shima
KDD2016勉強会 資料
KDD2016勉強会 https://atnd.org/events/80771
論文：“Why Should I Trust You?”Explaining the Predictions of Any Classifier
著者：M. T. Ribeiro and S. Singh and C. Guestrin
論文リンク： http://www.kdd.org/kdd2016/subtopic/view/why-should-i-trust-you-explaining-the-predictions-of-any-classifier
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ベイズ推定の基本とPyMCによる簡単な実装例です．
関連資料： https://github.com/scipy-japan/tokyo-scipy/tree/master/006/shima__shima
科学技術計算関連Pythonパッケージの概要
こちらが改訂版なので，ダウンロードするならこちらで： http://www.kamishima.net/archive/scipy-overview.pdf
第15回情報科学技術フォーラム (FIT2016) での講演「科学技術計算関連Pythonパッケージの概要」の発表資料です．
* 講演ページ： http://www.ipsj.or.jp/event/fit/fit2016/FIT2016program_web/data/html/event/event72.html
* 小嵜 耕平さん資料「Python とデータ分析コンテストの実践」 https://speakerdeck.com/smly/python-todetafen-xi-kontesutofalseshi-jian
* サンプルファイル： https://github.com/tkamishima/fit2016tutorial
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Fairness-aware Classifier with Prejudice Remover Regularizer
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strengthen your personal intention to open each chakra more fully. These are designed
to draw forth the highest benefit for your spiritual growth.
Why is this So? ~ Do Seek to KNOW (English & Chinese).pptx
A PowerPoint Presentation based on the Dhamma teaching of Kamma-Vipaka (Intentional Actions-Ripening Effects).
A Presentation for developing morality, concentration and wisdom and to spur us to practice the Dhamma diligently.
The texts are in English and Chinese.
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Nancy’s images of harmony
- 1. Nancy’s Images of Harmony From her world
- 9. Please feel free to send us your images and / or stories of what ‘HARMONY’ means to you. You can email Nancy on: [email_address]