Papers with Code 아카이브

Fairness

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

9

보존된 평가 기록

10

코드

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

https://github.com/rg223/praffl

공식 코드

PraFFL: A Preference-Aware Scheme in Fair Federated Learning

https://github.com/rh-dang/ecbench

공식 코드

ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark

https://github.com/richard-peng-xia/cares

공식 코드

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

https://github.com/richardobi/medigan

공식 코드

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

https://github.com/richrast/disparateuncertainty

공식 코드

Fairness in Ranking under Disparate Uncertainty

https://github.com/richrast/fairness_in_ranking_under_disparate_uncertainty

공식 코드

Fairness in Ranking under Disparate Uncertainty

https://github.com/rickymaulanafajri/fal-cur

공식 코드

FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

https://github.com/rik-helwegen/FairTrade

공식 코드

Improving Fair Predictions Using Variational Inference In Causal Models

https://github.com/riktor/exadmm-recommender

공식 코드

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems

https://github.com/riktor/safer2-recommender

공식 코드

Safe Collaborative Filtering

https://github.com/rmf3mc/covid-comp24

공식 코드

COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism

https://github.com/robbiemeyer/pw_loss_pruning

공식 코드

A Fair Loss Function for Network Pruning

https://github.com/roger-zhe-li/ictir23-cost-sensitive

공식 코드

Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning

https://github.com/romm32/sagnn

공식 코드

Wireless Link Scheduling with State-Augmented Graph Neural Networks

https://github.com/roypic/aligner

공식 코드

On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging

https://github.com/royull/fairedit

공식 코드

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

https://github.com/rperi/trustworthy-asv-fairness

공식 코드

To train or not to train adversarially: A study of bias mitigation strategies for speaker recognition

https://github.com/rsalganik1123/facctrec2022

공식 코드

Analyzing the Effect of Sampling in GNNs on Individual Fairness

https://github.com/rsdczhs0/TriProTesting

공식 코드

Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation

https://github.com/rssalessio/fair-best-arm-identification

공식 코드

Fair Best Arm Identification with Fixed Confidence

https://github.com/ruc-wjyu/iot-match

공식 코드

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

https://github.com/rucaibox/recbole2.0

공식 코드

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

https://github.com/rucy74/DAFA

공식 코드

DAFA: Distance-Aware Fair Adversarial Training

https://github.com/rugrag/learn-unbiased

공식 코드

Learning Unbiased Representations via Mutual Information Backpropagation

https://github.com/runedk93/pantypes

공식 코드

Pantypes: Diverse Representatives for Self-Explainable Models

https://github.com/runtianz/boosted_cvar

공식 코드

Boosted CVaR Classification

https://github.com/ruqizhang/o-gradient

공식 코드

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent

https://github.com/rutgerswiselab/user-fairness

공식 코드

User-oriented Fairness in Recommendation

https://github.com/rxian/fair-classification

공식 코드

Fair and Optimal Classification via Post-Processing

https://github.com/rxian/fair-regression

공식 코드

Differentially Private Post-Processing for Fair Regression

https://github.com/sachinpc1993/fair-arguments

공식 코드

Evaluating Fairness in Argument Retrieval

https://github.com/sacs-epfl/facade

공식 코드

Fair Decentralized Learning

https://github.com/sacs-epfl/fairness-audits-with-collaboration

공식 코드

Fairness Auditing with Multi-Agent Collaboration

https://github.com/sadrasabouri/sdp-fair-graph-clustering

공식 코드

A Semidefinite Relaxation Approach for Fair Graph Clustering

https://github.com/safr-ai-lab/xai-disparity

공식 코드

Feature Importance Disparities for Data Bias Investigations

https://github.com/saharaja/ICML2020-fairness

공식 코드

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics

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