Pythonによる機械学習実験の管理

•

42 j'aime•12,296 vues

Toshihiro Kamishima

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

Données & analyses Technologie Formation

•
•
•
•
• John K. Ousterhout “Scripting: Higher Level Programming for the 21st
Century”
•
•
•
2

•
•
•
•
•
3

•
•
•
•
4

•
•
•
5

•
•
•
•
•
6

•
•
•
7

•
•
8

•
•
•
•
9

•
10

•
11

•
•
•
•
12

•
•
•
•
13

•
•
•
•
•
•
•
•
•
•
14

•
•
•
•
15

•
•
16

•
•
•
17

•
•
•
•
•
•
•
•
18

•
•
•
•
•
19

•
•
•
•
20

•
•
•
•
•
•
21

•
•
•
•
•
22

Contenu connexe

Tendances

【DL輪読会】Prompting Decision Transformer for Few-Shot Policy Generalization

【DL輪読会】Prompting Decision Transformer for Few-Shot Policy Generalization

【DL輪読会】Prompting Decision Transformer for Few-Shot Policy Generalization

Deep Learning JP

Long-Tailed Classificationの最新動向について

Long-Tailed Classificationの最新動向について

Long-Tailed Classificationの最新動向について

[DL輪読会]Deep Learning 第2章線形代数

[DL輪読会]Deep Learning 第2章線形代数

[DL輪読会]Deep Learning 第2章線形代数

Deep Learning JP

動画認識における代表的なモデル・データセット（メタサーベイ）

動画認識における代表的なモデル・データセット（メタサーベイ）

動画認識における代表的なモデル・データセット（メタサーベイ）

cvpaper. challenge

[DL輪読会]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (ICCV2019)

[DL輪読会]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (ICCV2019)

[DL輪読会]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (ICCV2019)

Deep Learning JP

[DL Hacks]Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternati...

[DL Hacks]Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternati...

[DL Hacks]Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternati...

Deep Learning JP

[DL輪読会]相互情報量最大化による表現学習

[DL輪読会]相互情報量最大化による表現学習

[DL輪読会]相互情報量最大化による表現学習

Deep Learning JP

【DL輪読会】Free Lunch for Few-shot Learning: Distribution Calibration

【DL輪読会】Free Lunch for Few-shot Learning: Distribution Calibration

【DL輪読会】Free Lunch for Few-shot Learning: Distribution Calibration

Deep Learning JP

【DL輪読会】Flamingo: a Visual Language Model for Few-Shot Learning 画像×言語の大規模基盤モ...

【DL輪読会】Flamingo: a Visual Language Model for Few-Shot Learning 画像×言語の大規模基盤モ...

【DL輪読会】Flamingo: a Visual Language Model for Few-Shot Learning 画像×言語の大規模基盤モ...

Deep Learning JP

物体検出の歴史（R-CNNからSSD・YOLOまで）

物体検出の歴史（R-CNNからSSD・YOLOまで）

物体検出の歴史（R-CNNからSSD・YOLOまで）

HironoriKanazawa

最適輸送問題（Wasserstein 距離）を解く方法についてのさまざまなアプローチ・アルゴリズムを紹介します。線形計画を使った定式化の基礎からはじめて、以下の五つのアルゴリズムを紹介します。 1. ネットワークシンプレックス法 2. ハンガリアン法 3. Sinkhorn アルゴリズム 4. ニューラルネットワークによる推定 5. スライス法このスライドは第三回 0x-seminar https://sites.google.com/view/uda-0x-seminar/home/0x03 で使用したものです。自己完結するよう心がけたのでセミナーに参加していない人にも役立つスライドになっています。『最適輸送の理論とアルゴリズム』好評発売中！ https://www.amazon.co.jp/dp/4065305144 Speakerdeck にもアップロードしました: https://speakerdeck.com/joisino/zui-shi-shu-song-nojie-kifang

最適輸送の解き方

【DL輪読会】StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-I...

【DL輪読会】StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-I...

【DL輪読会】StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-I...

Deep Learning JP

画像生成・生成モデルメタサーベイ

画像生成・生成モデルメタサーベイ

画像生成・生成モデルメタサーベイ

cvpaper. challenge

[DL輪読会]“SimPLe”,“Improved Dynamics Model”,“PlaNet” 近年のVAEベース系列モデルの進展とそのモデルベース...

[DL輪読会]“SimPLe”,“Improved Dynamics Model”,“PlaNet” 近年のVAEベース系列モデルの進展とそのモデルベース...

[DL輪読会]“SimPLe”,“Improved Dynamics Model”,“PlaNet” 近年のVAEベース系列モデルの進展とそのモデルベース...

Deep Learning JP

[DL輪読会]A closer look at few shot classification

[DL輪読会]A closer look at few shot classification

[DL輪読会]A closer look at few shot classification

Deep Learning JP

Introduction to YOLO detection model

Introduction to YOLO detection model

Introduction to YOLO detection model

WEBFARMER. ltd.

MS COCO Dataset Introduction

MS COCO Dataset Introduction

MS COCO Dataset Introduction

Shinagawa Seitaro

ICML2018読み会: Overview of NLP / Adversarial Attacks

ICML2018読み会: Overview of NLP / Adversarial Attacks

ICML2018読み会: Overview of NLP / Adversarial Attacks

[DL輪読会]Deep Learning 第15章表現学習

[DL輪読会]Deep Learning 第15章表現学習

[DL輪読会]Deep Learning 第15章表現学習

Deep Learning JP

[DL輪読会]Geometric Unsupervised Domain Adaptation for Semantic Segmentation

[DL輪読会]Geometric Unsupervised Domain Adaptation for Semantic Segmentation

[DL輪読会]Geometric Unsupervised Domain Adaptation for Semantic Segmentation

Deep Learning JP

Tendances (20)

【DL輪読会】Prompting Decision Transformer for Few-Shot Policy Generalization

【DL輪読会】Prompting Decision Transformer for Few-Shot Policy Generalization

【DL輪読会】Prompting Decision Transformer for Few-Shot Policy Generalization

Long-Tailed Classificationの最新動向について

Long-Tailed Classificationの最新動向について

Long-Tailed Classificationの最新動向について

[DL輪読会]Deep Learning 第2章線形代数

[DL輪読会]Deep Learning 第2章線形代数

[DL輪読会]Deep Learning 第2章線形代数

動画認識における代表的なモデル・データセット（メタサーベイ）

動画認識における代表的なモデル・データセット（メタサーベイ）

動画認識における代表的なモデル・データセット（メタサーベイ）

[DL輪読会]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (ICCV2019)

[DL輪読会]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (ICCV2019)

[DL輪読会]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (ICCV2019)

[DL Hacks]Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternati...

[DL Hacks]Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternati...

[DL Hacks]Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternati...

[DL輪読会]相互情報量最大化による表現学習

[DL輪読会]相互情報量最大化による表現学習

[DL輪読会]相互情報量最大化による表現学習

【DL輪読会】Free Lunch for Few-shot Learning: Distribution Calibration

【DL輪読会】Free Lunch for Few-shot Learning: Distribution Calibration

【DL輪読会】Free Lunch for Few-shot Learning: Distribution Calibration

【DL輪読会】Flamingo: a Visual Language Model for Few-Shot Learning 画像×言語の大規模基盤モ...

【DL輪読会】Flamingo: a Visual Language Model for Few-Shot Learning 画像×言語の大規模基盤モ...

【DL輪読会】Flamingo: a Visual Language Model for Few-Shot Learning 画像×言語の大規模基盤モ...

物体検出の歴史（R-CNNからSSD・YOLOまで）

物体検出の歴史（R-CNNからSSD・YOLOまで）

物体検出の歴史（R-CNNからSSD・YOLOまで）

最適輸送の解き方

【DL輪読会】StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-I...

【DL輪読会】StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-I...

【DL輪読会】StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-I...

画像生成・生成モデルメタサーベイ

画像生成・生成モデルメタサーベイ

画像生成・生成モデルメタサーベイ

[DL輪読会]“SimPLe”,“Improved Dynamics Model”,“PlaNet” 近年のVAEベース系列モデルの進展とそのモデルベース...

[DL輪読会]“SimPLe”,“Improved Dynamics Model”,“PlaNet” 近年のVAEベース系列モデルの進展とそのモデルベース...

[DL輪読会]“SimPLe”,“Improved Dynamics Model”,“PlaNet” 近年のVAEベース系列モデルの進展とそのモデルベース...

[DL輪読会]A closer look at few shot classification

[DL輪読会]A closer look at few shot classification

[DL輪読会]A closer look at few shot classification

Introduction to YOLO detection model

Introduction to YOLO detection model

Introduction to YOLO detection model

MS COCO Dataset Introduction

MS COCO Dataset Introduction

MS COCO Dataset Introduction

ICML2018読み会: Overview of NLP / Adversarial Attacks

ICML2018読み会: Overview of NLP / Adversarial Attacks

ICML2018読み会: Overview of NLP / Adversarial Attacks

[DL輪読会]Deep Learning 第15章表現学習

[DL輪読会]Deep Learning 第15章表現学習

[DL輪読会]Deep Learning 第15章表現学習

[DL輪読会]Geometric Unsupervised Domain Adaptation for Semantic Segmentation

[DL輪読会]Geometric Unsupervised Domain Adaptation for Semantic Segmentation

[DL輪読会]Geometric Unsupervised Domain Adaptation for Semantic Segmentation

Plus de Toshihiro Kamishima

RecSys2018論文読み会資料

RecSys2018論文読み会資料

RecSys2018論文読み会資料

Toshihiro Kamishima

WSDM2018読み会資料

Toshihiro Kamishima

Recommendation Independence The 1st Conference on Fairness, Accountability, and Transparency Article @ Official Site: http://proceedings.mlr.press/v81/kamishima18a.html Conference site: https://fatconference.org/2018/ Abstract: This paper studies a recommendation algorithm whose outcomes are not influenced by specified information. It is useful in contexts potentially unfair decision should be avoided, such as job-applicant recommendations that are not influenced by socially sensitive information. An algorithm that could exclude the influence of sensitive information would thus be useful for job-matching with fairness. We call the condition between a recommendation outcome and a sensitive feature Recommendation Independence, which is formally defined as statistical independence between the outcome and the feature. Our previous independence-enhanced algorithms simply matched the means of predictions between sub-datasets consisting of the same sensitive value. However, this approach could not remove the sensitive information represented by the second or higher moments of distributions. In this paper, we develop new methods that can deal with the second moment, i.e., variance, of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that our new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, we explore potential applications for independence-enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity.

Recommendation Independence

Recommendation Independence

Recommendation Independence

Toshihiro Kamishima

機械学習研究でのPythonの利用

機械学習研究でのPythonの利用

機械学習研究でのPythonの利用

Toshihiro Kamishima

Considerations on Recommendation Independence for a Find-Good-Items Task Workshop on Responsible Recommendation (FATREC), in conjunction with RecSys2017 Article @ Official Site: http://doi.org/10.18122/B2871W Workshop Homepage: https://piret.gitlab.io/fatrec/ This paper examines the notion of recommendation independence, which is a constraint that a recommendation result is independent from specific information. This constraint is useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, to make a job-matching recommendation socially fair, the matching should be independent of socially sensitive information, such as gender or race. We previously developed several recommenders satisfying recommendation independence, but these were all designed for a predicting-ratings task, whose goal is to predict a score that a user would rate. We here focus on another find-good-items task, which aims to find some items that a user would prefer. In this task, scores representing the degree of preference to items are first predicted, and some items having the largest scores are displayed in the form of a ranked list. We developed a preliminary algorithm for this task through a naive approach, enhancing independence between a preference score and sensitive information. We empirically show that although this algorithm can enhance independence of a preference score, it is not fit for the purpose of enhancing independence in terms of a ranked list. This result indicates the need for inventing a notion of independence that is suitable for use with a ranked list and that is applicable for completing a find-good-items task.

Considerations on Recommendation Independence for a Find-Good-Items Task

Considerations on Recommendation Independence for a Find-Good-Items Task

Considerations on Recommendation Independence for a Find-Good-Items Task

Toshihiro Kamishima

Model-based Approaches for Independence-Enhanced Recommendation IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2016 Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2016.0127 Workshop Homepage: http://pddm16.eurecat.org/ Abstract: This paper studies a new approach to enhance recommendation independence. Such approaches are useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, recommendations that match an employer with a job applicant should not be based on socially sensitive information, such as gender or race, from the perspective of social fairness. An algorithm that could exclude the influence of such sensitive information would be useful in this case. We previously gave a formal definition of recommendation independence and proposed a method adopting a regularizer that imposes such an independence constraint. As no other options than this regularization approach have been put forward, we here propose a new model-based approach, which is based on a generative model that satisfies the constraint of recommendation independence. We apply this approach to a latent class model and empirically show that the model-based approach can enhance recommendation independence. Recommendation algorithms based on generative models, such as topic models, are important, because they have a flexible functionality that enables them to incorporate a wide variety of information types. Our new model-based approach will broaden the applications of independence-enhanced recommendation by integrating the functionality of generative models.

Model-based Approaches for Independence-Enhanced Recommendation

Model-based Approaches for Independence-Enhanced Recommendation

Model-based Approaches for Independence-Enhanced Recommendation

Toshihiro Kamishima

KDD2016勉強会資料

Toshihiro Kamishima

WSDM2016勉強会資料

Toshihiro Kamishima

ICML2015読み会資料

Toshihiro Kamishima

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

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...

Toshihiro Kamishima

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.

Correcting Popularity Bias by Enhancing Recommendation Neutrality

Correcting Popularity Bias by Enhancing Recommendation Neutrality

Correcting Popularity Bias by Enhancing Recommendation Neutrality

Toshihiro Kamishima

PyMCがあれば，ベイズ推定でもう泣いたりなんかしない

PyMCがあれば，ベイズ推定でもう泣いたりなんかしない

PyMCがあれば，ベイズ推定でもう泣いたりなんかしない

Toshihiro Kamishima

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.

The Independence of Fairness-aware Classifiers

The Independence of Fairness-aware Classifiers

The Independence of Fairness-aware Classifiers

Toshihiro Kamishima

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.

Efficiency Improvement of Neutrality-Enhanced Recommendation

Efficiency Improvement of Neutrality-Enhanced Recommendation

Efficiency Improvement of Neutrality-Enhanced Recommendation

Toshihiro Kamishima

Absolute and Relative Clustering 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (Multiclust 2013) Aug. 11, 2013 @ Chicago, U.S.A, in conjunction with KDD2013 Article @ Official Site: http://dx.doi.org/10.1145/2501006.2501013 Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-kdd-print.pdf Handnote: http://www.kamishima.net/archive/2013-ws-kdd-HN.pdf Workshop Homepage: http://cs.au.dk/research/research-areas/data-intensive-systems/projects/multiclust2013/ Abstract: Research into (semi-)supervised clustering has been increasing. Supervised clustering aims to group similar data that are partially guided by the user's supervision. In this supervised clustering, there are many choices for formalization. For example, as a type of supervision, one can adopt labels of data points, must/cannot links, and so on. Given a real clustering task, such as grouping documents or image segmentation, users must confront the question ``How should we mathematically formalize our task?''To help answer this question, we propose the classification of real clusterings into absolute and relative clusterings, which are defined based on the relationship between the resultant partition and the data set to be clustered. This categorization can be exploited to choose a type of task formalization.

Absolute and Relative Clustering

Absolute and Relative Clustering

Absolute and Relative Clustering

Toshihiro Kamishima

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.

Consideration on Fairness-aware Data Mining

Consideration on Fairness-aware Data Mining

Consideration on Fairness-aware Data Mining

Toshihiro Kamishima

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.

Fairness-aware Classifier with Prejudice Remover Regularizer

Fairness-aware Classifier with Prejudice Remover Regularizer

Fairness-aware Classifier with Prejudice Remover Regularizer

Toshihiro Kamishima

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.

Enhancement of the Neutrality in Recommendation

Enhancement of the Neutrality in Recommendation

Enhancement of the Neutrality in Recommendation

Toshihiro Kamishima

OpenOpt の線形計画で圧縮センシング

OpenOpt の線形計画で圧縮センシング

OpenOpt の線形計画で圧縮センシング

Toshihiro Kamishima

Fairness-aware Learning through Regularization Approach The 3rd IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2011) Dec. 11, 2011 @ Vancouver, Canada, in conjunction with ICDM2011 Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2011.83 Article @ Personal Site: http://www.kamishima.net/archive/2011-ws-icdm_padm.pdf Handnote: http://www.kamishima.net/archive/2011-ws-icdm_padm-HN.pdf Workshop Homepage: http://www.zurich.ibm.com/padm2011/ 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 people's 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 socially and legally fair from a viewpoint of social responsibility; namely, it must be unbiased and nondiscriminatory 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. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. 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.

Fairness-aware Learning through Regularization Approach

Fairness-aware Learning through Regularization Approach

Fairness-aware Learning through Regularization Approach

Toshihiro Kamishima

Plus de Toshihiro Kamishima (20)

RecSys2018論文読み会資料

RecSys2018論文読み会資料

RecSys2018論文読み会資料

WSDM2018読み会資料

Recommendation Independence

Recommendation Independence

Recommendation Independence

機械学習研究でのPythonの利用

機械学習研究でのPythonの利用

機械学習研究でのPythonの利用

Considerations on Recommendation Independence for a Find-Good-Items Task

Considerations on Recommendation Independence for a Find-Good-Items Task

Considerations on Recommendation Independence for a Find-Good-Items Task

Model-based Approaches for Independence-Enhanced Recommendation

Model-based Approaches for Independence-Enhanced Recommendation

Model-based Approaches for Independence-Enhanced Recommendation

KDD2016勉強会資料

WSDM2016勉強会資料

ICML2015読み会資料

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...

Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...

Correcting Popularity Bias by Enhancing Recommendation Neutrality

Correcting Popularity Bias by Enhancing Recommendation Neutrality

Correcting Popularity Bias by Enhancing Recommendation Neutrality

PyMCがあれば，ベイズ推定でもう泣いたりなんかしない

PyMCがあれば，ベイズ推定でもう泣いたりなんかしない

PyMCがあれば，ベイズ推定でもう泣いたりなんかしない

The Independence of Fairness-aware Classifiers

The Independence of Fairness-aware Classifiers

The Independence of Fairness-aware Classifiers

Efficiency Improvement of Neutrality-Enhanced Recommendation

Efficiency Improvement of Neutrality-Enhanced Recommendation

Efficiency Improvement of Neutrality-Enhanced Recommendation

Absolute and Relative Clustering

Absolute and Relative Clustering

Absolute and Relative Clustering

Consideration on Fairness-aware Data Mining

Consideration on Fairness-aware Data Mining

Consideration on Fairness-aware Data Mining

Fairness-aware Classifier with Prejudice Remover Regularizer

Fairness-aware Classifier with Prejudice Remover Regularizer

Fairness-aware Classifier with Prejudice Remover Regularizer

Enhancement of the Neutrality in Recommendation

Enhancement of the Neutrality in Recommendation

Enhancement of the Neutrality in Recommendation

OpenOpt の線形計画で圧縮センシング

OpenOpt の線形計画で圧縮センシング

OpenOpt の線形計画で圧縮センシング

Fairness-aware Learning through Regularization Approach

Fairness-aware Learning through Regularization Approach

Fairness-aware Learning through Regularization Approach

Pythonによる機械学習実験の管理

2. • • • • • John K. Ousterhout “Scripting: Higher Level Programming for the 21st Century” • • • 2

3. • • • • • 3

4. • • • • 4

5. • • • 5

6. • • • • • 6

7. • • • 7

9. • • • • 9

12. • • • • 12

13. • • • • 13

14. • • • • • • • • • • 14

15. • • • • 15

17. • • • 17

18. • • • • • • • • 18

19. • • • • • 19

20. • • • • 20

21. • • • • • • 21

22. • • • • • 22