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Selection bias

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

https://github.com/csinva/matching-with-gans

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Matched sample selection with GANs for mitigating attribute confounding

https://github.com/csquires/dct-policy

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Active Structure Learning of Causal DAGs via Directed Clique Tree

https://github.com/cynthpie/synthia_melody

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Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio

https://github.com/davidinouye/adp

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Automated Dependence Plots

https://github.com/dmirlab-group/rest

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REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

https://github.com/edisonni-hku/diras

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DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation

https://github.com/ehsan-mokhtarian/l-marvel

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Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias

https://github.com/fanyunfeng-bit/balanced-modality-selection-in-mfl

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Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection

https://github.com/ghzhang233/Non-Discrimination-Learning-for-Text-Classification

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Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting

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

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Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness

https://github.com/googlebaba/IJCAI2020-DCKM

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Decorrelated Clustering with Data Selection Bias

https://github.com/greek-nlp/survey

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Towards Systematic Monolingual NLP Surveys: GenA of Greek NLP

https://github.com/guosyjlu/MRDR-DL

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Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation

https://github.com/haoxuanli-pku/iclr24-interference

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Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference

https://github.com/heyyyyyyg/cfie

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Uncovering Main Causalities for Long-tailed Information Extraction

https://github.com/hlzhang109/transtee

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Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation

https://github.com/hyz20/cld

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Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition

https://github.com/idsia-papers/2022-pgm-selection

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Bounding Counterfactuals under Selection Bias

https://github.com/idsia-papers/2023-ijar-bias-hybrid

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Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data

https://github.com/iml-dkfz/latec

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Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics

https://github.com/jayjayc/scary

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The Structurally Complex with Additive Parent Causality (SCARY) Dataset

https://github.com/jenninglim/multiscale-features

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More Powerful Selective Kernel Tests for Feature Selection

https://github.com/jiawei-chen/RecDebiasing

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Bias and Debias in Recommender System: A Survey and Future Directions

https://github.com/jjfeng/monitoring_ml_cmi

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Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions

https://github.com/jmdvinodjmd/SSB_MLHC

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Sample Selection Bias in Machine Learning for Healthcare

https://github.com/joanagoncalveslab/DCAST

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DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning

https://github.com/joanagoncalveslab/metric-dst

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Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning

https://github.com/jordan-hs/rss-interference-cvprw2022

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Does Interference Exist When Training a Once-For-All Network?

https://github.com/jshang123/G-Bert

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Pre-training of Graph Augmented Transformers for Medication Recommendation

https://github.com/jvpoulos/homesteads

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State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction

https://github.com/kamruzzaman15/identifying-subtler-biases-in-llms

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Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models

https://github.com/kanekomasahiro/evaluate_bias_in_mlm

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Unmasking the Mask -- Evaluating Social Biases in Masked Language Models

https://github.com/kikimormay/fsl-tcbr

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Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

https://github.com/koh-t/SC-CFR

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Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference

https://github.com/kolyanray/Bayesian-Causal-Inference

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Debiased Bayesian inference for average treatment effects

https://github.com/l294265421/SIGIR22-TOWE

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Training Entire-Space Models for Target-oriented Opinion Words Extraction

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