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Fairness

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보존된 평가 기록

10

코드

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

https://github.com/optimization-for-data-driven-science/FERMI

공식 코드

A Stochastic Optimization Framework for Fair Risk Minimization

https://github.com/optimization-for-data-driven-science/f-ferm

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f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization

https://github.com/optmn-lab/fairgrad

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Fair Resource Allocation in Multi-Task Learning

https://github.com/oscardilley/federated-fairness

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Federated Fairness Analytics: Quantifying Fairness in Federated Learning

https://github.com/osu-nlp-group/eval-llm-trust

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How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities

https://github.com/osu-srml/LCF

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Lookahead Counterfactual Fairness

https://github.com/osu-srml/Unforeseeable-SC

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Learning under Imitative Strategic Behavior with Unforeseeable Outcomes

https://github.com/osu-srml/automating-data-annotation-under-strategic-human-agents

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Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions

https://github.com/osu-srml/cf_representation_learning

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Counterfactually Fair Representation

https://github.com/osu-srml/svd_fairness

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Post-processing for Fair Regression via Explainable SVD

https://github.com/oxai/world-wide-dishes

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The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation

https://github.com/oxfordinternetinstitute/oxonfair

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OxonFair: A Flexible Toolkit for Algorithmic Fairness

https://github.com/oxkitsune/fact

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Reproducibility study of FairAC

https://github.com/paganick/feedback-loops-and-biases

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A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems

https://github.com/pair-code/what-if-tool

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The What-If Tool: Interactive Probing of Machine Learning Models

https://github.com/panagiotou/fairaugment

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Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study

https://github.com/panayiotisd/fisher-market

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AI-driven Prices for Externalities and Sustainability in Production Markets

https://github.com/panzhaooo/positivity-free-policy-learning

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Positivity-free Policy Learning with Observational Data

https://github.com/papersubmission678/the-cost-of-local-and-global-fairness-in-fl

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The Cost of Local and Global Fairness in Federated Learning

https://github.com/paramita08/wikidata_auditlp

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Social Biases in Knowledge Representations of Wikidata separates Global North from Global South

https://github.com/parkjinhyeock/evalrs-submission

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Item-based Variational Auto-encoder for Fair Music Recommendation

https://github.com/participatory-budgeting/maxmin_2022

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Maxmin Participatory Budgeting

https://github.com/pasta41/hmda

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Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification

https://github.com/patrikken/fair-dsr

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Fairness Under Demographic Scarce Regime

https://github.com/penfever/tabpfn-pt

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TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks

https://github.com/penfever/tunetables

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TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks

https://github.com/pengyanghua/DL2

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DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters

https://github.com/pennshenlab/fair_gms

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Fairness-Aware Estimation of Graphical Models

https://github.com/pennswood/fairlay-ml

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FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms

https://github.com/pereirabarataap/fair_tree_classifier

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Fair Tree Classifier using Strong Demographic Parity

https://github.com/petuum/adaptdl

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Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning

https://github.com/petuum/pollux-results

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Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning

https://github.com/phi-ra/SequentialFairness

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A Sequentially Fair Mechanism for Multiple Sensitive Attributes

https://github.com/phi-ra/fairmultitask

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Fairness in Multi-Task Learning via Wasserstein Barycenters

https://github.com/philips-george/p-mean-regret-stochastic-bandits

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p-Mean Regret for Stochastic Bandits

https://github.com/picsart-ai-research/openbias

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OpenBias: Open-set Bias Detection in Text-to-Image Generative Models

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