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Fairness

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/google-research/disentanglement_lib

공식 코드

Weakly-Supervised Disentanglement Without Compromises

https://github.com/google-research/federated

공식 코드

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

https://github.com/google-research/federated/tree/master/personalization_benchmark

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Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

https://github.com/google-research/google-research

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Pairwise Fairness for Ranking and Regression

https://github.com/google-research/google-research/tree/master/correlation_clustering

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Fair Correlation Clustering

https://github.com/google-research/google-research/tree/master/group_agnostic_fairness

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Fairness without Demographics through Adversarially Reweighted Learning

https://github.com/google-research/google-research/tree/master/pruning_identified_exemplars

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What Do Compressed Deep Neural Networks Forget?

https://github.com/google-research/kubric

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Kubric: A scalable dataset generator

https://github.com/google-research/socialgood

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Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health

https://github.com/google-research/url-nlp

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Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold

https://github.com/google/uafcs

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Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples

https://github.com/gourabkumarpatro/FairRec_www_2020

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FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

https://github.com/gourabkumarpatro/fairconf

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Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction

https://github.com/gp1g/fedaa

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FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning

https://github.com/gpleiss/equalized_odds_and_calibration

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On Fairness and Calibration

https://github.com/gradientinstitute/fastfair

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Fast Fair Regression via Efficient Approximations of Mutual Information

https://github.com/graphpku/open-world-kg

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Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

https://github.com/grey-z/fairrec

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FairRec: Fairness Testing for Deep Recommender Systems

https://github.com/grf-labs/policyTree

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Policy Learning with Observational Data

https://github.com/groshanlal/Fairness-Aware-Online-Personalization

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Fairness-Aware Online Personalization

https://github.com/gsarridis/faceX

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FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations

https://github.com/gsarridis/flac

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FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations

https://github.com/guanchuwang/apod-fairness

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Mitigating Algorithmic Bias with Limited Annotations

https://github.com/guijinSON/MM-Eval

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MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Models

https://github.com/guilhermepelegrina/fpca

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A novel approach for Fair Principal Component Analysis based on eigendecomposition

https://github.com/guldoganozgur/ei_fairness

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Equal Improvability: A New Fairness Notion Considering the Long-term Impact

https://github.com/guldoganozgur/long_term_fairness_pos_reinf

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Long-Term Fairness in Sequential Multi-Agent Selection with Positive Reinforcement

https://github.com/guruprerana/worm

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Composing Agents to Minimize Worst-case Risk

https://github.com/guxm2021/svt_speechbrain

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Elucidate Gender Fairness in Singing Voice Transcription

https://github.com/h2oai/h2o-llmstudio

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h2oGPT: Democratizing Large Language Models

https://github.com/hahhacx/In-UCDS

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Reproducibility Companion Paper:In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

https://github.com/haibinzheng/neuronfair

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NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

https://github.com/haihua0913/awesome-dq4ml

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A Survey on Data Quality Dimensions and Tools for Machine Learning

https://github.com/hammerlabml/modelagnosticgroupfairnesscounterfactuals

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"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations

https://github.com/hannahxchen/composed-debiasing

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Addressing Both Statistical and Causal Gender Fairness in NLP Models

https://github.com/hansen7/LearnFairMetric_Empirical

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An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision

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