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Fairness

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코드

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

https://github.com/yyzharry/shortcut-ood-fairness

공식 코드

The Limits of Fair Medical Imaging AI In The Wild

https://github.com/yyzharry/vlm-fairness

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Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging

https://github.com/yzeng58/improving-fairness-via-data-federation

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Improving Fairness via Federated Learning

https://github.com/yzeng58/improving-fairness-via-federated-learning

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Improving Fairness via Federated Learning

https://github.com/yzhang1918/cikm2022rudi

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RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation

https://github.com/yzliu567/sc-gan

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Self-Conditioned Generative Adversarial Networks for Image Editing

https://github.com/zarahall/fairness-prms

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Guiding LLM Decision-Making with Fairness Reward Models

https://github.com/zengqunzhao/aim-fair

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AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data

https://github.com/zeyutang/decoupleobjectionable

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Procedural Fairness Through Decoupling Objectionable Data Generating Components

https://github.com/zeyutang/tierbalancing

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Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

https://github.com/zgahhblhc/DialogueFairness

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Does Gender Matter? Towards Fairness in Dialogue Systems

https://github.com/zhang-l-i-n/taxonomy_detect

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LITE: LLM-Impelled efficient Taxonomy Evaluation

https://github.com/zhangbinchi/g-fairattack

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Adversarial Attacks on Fairness of Graph Neural Networks

https://github.com/zhanghao5201/b-avibench

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B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-Instructions

https://github.com/zhaoshiji123/absld

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Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation

https://github.com/zhengyjo/cfair

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Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation

https://github.com/zhengyunhan/equality-of-opportunity-travel-behavior

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Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models

https://github.com/zhenhuan-yang/minimaxfairauc

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Minimax AUC Fairness: Efficient Algorithm with Provable Convergence

https://github.com/zhenweilin/constrainedgbm

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GBM-based Bregman Proximal Algorithms for Constrained Learning

https://github.com/zhihengli-UR/StyleT2I

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StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis

https://github.com/zhimengj0326/fmp

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Chasing Fairness in Graphs: A GNN Architecture Perspective

https://github.com/zhimengj0326/rfr_neurips23

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Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach

https://github.com/zhiningliu1998/aim

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AIM: Attributing, Interpreting, Mitigating Data Unfairness

https://github.com/zhiqix/teach2eval

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Teach2Eval: An Indirect Evaluation Method for LLM by Judging How It Teaches

https://github.com/zhixunlee/fairgb

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Rethinking Fair Graph Neural Networks from Re-balancing

https://github.com/zhiyuanyaoj/marllb

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Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

https://github.com/zhuxinyu2700/afrl

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Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment

https://github.com/zidixiu/delphi

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DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

https://github.com/zinka88/Fair-Regression

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Fair Regression for Health Care Spending

https://github.com/ziqunchen/fair_cmab_with_feedback_delays

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Merit-based Fair Combinatorial Semi-Bandit with Unrestricted Feedback Delays

https://github.com/zli12321/awesome-vlm-papers-and-models

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A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

https://github.com/zli12321/vision-language-models-overview

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A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges

https://github.com/zlzhan011/FSFS

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Fairness-Aware Streaming Feature Selection with Causal Graphs

https://github.com/zoedesimone/diffusionworldviewer

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DiffusionWorldViewer: Exposing and Broadening the Worldview Reflected by Generative Text-to-Image Models

https://github.com/zsk66/flame

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On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

https://github.com/zxlovely/FedKF

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Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.