Data Acquisition for Improving Model Fairness using Reinforcement Learning | Paper Citation Record

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This paper cites Analysis of thompson sampling for the multi-armed bandit problem.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Analysis of thompson sampling for the multi-armed bandit problem

Reference 1

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This paper cites Machine bias: There’s software used across the country to predict future criminals.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Machine bias: There’s software used across the country to predict future criminals

Reference 2

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Unresolved cited work

Reference 3

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This paper cites Using upper confidence bounds for online learning.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Using upper confidence bounds for online learning

Reference 4

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Using confidence bounds for exploitation-exploration trade-offs

Reference 5

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This paper cites Learn2clean: Optimizing the sequence of tasks for web data preparation.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Learn2clean: Optimizing the sequence of tasks for web data preparation

Reference 6

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This paper cites The shapley value in database manage- ment.

Data Acquisition for Improving Model Fairness using Reinforcement Learning The shapley value in database manage- ment

Reference 7

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This paper cites Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline

Reference 8

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This paper cites The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large.

Data Acquisition for Improving Model Fairness using Reinforcement Learning The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large

Reference 9

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This paper cites Housing department slaps facebook with discrimination charge.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Housing department slaps facebook with discrimination charge

Reference 10

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This paper cites Bandits with heavy tail.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Bandits with heavy tail

Reference 11

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This paper cites A survey on deep reinforcement learning for data processing and analytics.

Data Acquisition for Improving Model Fairness using Reinforcement Learning A survey on deep reinforcement learning for data processing and analytics

Reference 12

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This paper cites Upper- confidence-bound algorithms for active learning in multi-armed bandits.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Upper- confidence-bound algorithms for active learning in multi-armed bandits

Reference 13

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This paper cites Fairness in machine learning: A survey.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Fairness in machine learning: A survey

Reference 14

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This paper cites Selective data acquisition in the wild for model charging.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Selective data acquisition in the wild for model charging

Reference 15

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This paper cites Fair prediction with disparate impact: A study of bias in recidivism prediction instruments.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Reference 16

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This paper cites Dennis Cook and Sanford Weisberg.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Dennis Cook and Sanford Weisberg

Reference 17

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This paper cites Give me some credit, 2011.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Give me some credit, 2011

Reference 18

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This paper cites Rpt-insight-amazon scraps secret ai recruiting tool that showed bias against women.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Rpt-insight-amazon scraps secret ai recruiting tool that showed bias against women

Reference 19

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This paper cites Davis, S.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Davis, S

Reference 20

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This paper cites Explanations for data repair through shapley values.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Explanations for data repair through shapley values

Reference 21

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This paper cites Retiring adult: New datasets for fair machine learning.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Retiring adult: New datasets for fair machine learning

Reference 22

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This paper cites Uci machine learning repository.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Uci machine learning repository

Reference 23

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This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

Data Acquisition for Improving Model Fairness using Reinforcement Learning A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 24

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This paper cites Joint entity linking with deep reinforcement learning.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Joint entity linking with deep reinforcement learning

Reference 25

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Unresolved cited work

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Unresolved cited work

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This paper cites Data shapley: Equitable valuation of data for machine learning.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Data shapley: Equitable valuation of data for machine learning

Reference 28

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This paper cites On upper-confidence bound policies for switching bandit problems.

Data Acquisition for Improving Model Fairness using Reinforcement Learning On upper-confidence bound policies for switching bandit problems

Reference 29

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This paper cites Deepline: Automl tool for pipelines generation using deep reinforcement learning and hierarchical actions filtering.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Deepline: Automl tool for pipelines generation using deep reinforcement learning and hierarchical actions filtering

Reference 30

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Self-driving cars more likely to hit blacks

Reference 31

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Data clustering: 50 years beyond k-means

Reference 32

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Unresolved cited work

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Understanding black-box predictions via influence functions

Reference 34

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Unresolved cited work

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Qtune: a query-aware database tuning system with deep reinforcement learning

Reference 36

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Algorithms for multi-armed bandit problems

Reference 37

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Data acquisition for improving model confidence

Reference 38

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Data Acquisition for Improving Machine Learning Models

Reference 39

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This paper cites A Survey on Bias and Fairness in Machine Learning.

Data Acquisition for Improving Model Fairness using Reinforcement Learning A Survey on Bias and Fairness in Machine Learning

Reference 40

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Applications and computation of the shapley value in databases and machine learning

Reference 41

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Tailoring data source distributions for fairness-aware data integration

Reference 42

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Improving information extraction by acquiring external evidence with reinforcement learning

Reference 43

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This paper cites Recommendation System-based Upper Confidence Bound for Online Advertising.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Recommendation System-based Upper Confidence Bound for Online Advertising

Reference 44

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Data Acquisition for Improving Model Fairness using Reinforcement Learning The bayesian information criterion: background, derivation, and applications

Reference 45

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Scikit-learn: Machine learning in python

Reference 46

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Pytorch: An imperative style, high-performance deep learning library

Reference 47

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This paper cites Interpretable Data-Based Explanations for Fairness Debugging.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Interpretable Data-Based Explanations for Fairness Debugging

Reference 48

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This paper cites What can Data-Centric AI Learn from Data and ML Engineering?.

Data Acquisition for Improving Model Fairness using Reinforcement Learning What can Data-Centric AI Learn from Data and ML Engineering?

Reference 49

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Coverage-based data-centric approaches for responsible and trustworthy ai

Reference 50

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Sourcesight: Enabling effective source selection

Reference 51

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Unresolved cited work

Reference 52

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This paper cites Representation bias in data: A survey on identifica- tion and resolution techniques.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Representation bias in data: A survey on identifica- tion and resolution techniques

Reference 53

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This paper cites Reinforcement learning: An introduction.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Reinforcement learning: An introduction

Reference 54

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Data Acquisition for Improving Model Fairness using Reinforcement Learning Introduction to multi-armed bandits

Reference 55

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This paper cites Fairness definitions explained.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Fairness definitions explained

Reference 56

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This paper cites Slice tuner: A selective data acquisition framework for accurate and fair machine learning models.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Slice tuner: A selective data acquisition framework for accurate and fair machine learning models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.957067Z

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Observation 190d4e57-1ea9-4f79-a71a-f7b310163309 · outbound

This paper cites Op- timizing data acquisition to enhance machine learning performance.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Op- timizing data acquisition to enhance machine learning performance

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.933034Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 36e09389-b2ba-4d53-975a-bcdea56682c8 · outbound

This paper cites Multi-armed bandit algorithms and empirical evaluation.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Multi-armed bandit algorithms and empirical evaluation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.941214Z

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Observation a627085e-7bc7-45ad-b2a6-d37a3d010690 · outbound

This paper cites Data collection and quality challenges in deep learning: a data-centric ai perspective.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Data collection and quality challenges in deep learning: a data-centric ai perspective

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.915578Z

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Observation e7cd3417-72e4-4fb5-bc0d-c4a96e41789b · outbound

This paper cites Data discovery.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Data discovery

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.924232Z

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Observation da2dfff4-64b1-4429-a032-8284c7614789 · outbound

This paper cites Data-centric ai: Techniques and future perspectives.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Data-centric ai: Techniques and future perspectives

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.897102Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 89d9d2fe-f405-4cb0-b120-bec7fa4db608 · outbound

This paper cites Data-centric AI: Perspectives and Challenges, pages 945–948.

Data Acquisition for Improving Model Fairness using Reinforcement Learning Data-centric AI: Perspectives and Challenges, pages 945–948

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.906360Z

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Observation b61fbd86-1c3d-40d9-aef3-46ada582882b · outbound

This paper cites An end-to-end automatic cloud database tuning system using deep reinforcement learning.

Data Acquisition for Improving Model Fairness using Reinforcement Learning An end-to-end automatic cloud database tuning system using deep reinforcement learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:55:49.887261Z

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Observation 330b1894-ff65-417c-90ed-0963fc42fe7d · outbound

This paper cites ISSN: 2375-026X.

Data Acquisition for Improving Model Fairness using Reinforcement Learning ISSN: 2375-026X

Reference 2019

Resolution: parse uncertain; raw_fallback, observed 2026-08-11T22:55:50.327128Z

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