{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/fairness/papers/33","list_of":"/task/fairness","task":"Fairness","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":33,"pages_in_order":57,"rows_per_page":100,"rows":[3201,3300],"of":5676,"counts":{"archive_papers_tagged":5676,"with_a_code_link":1714,"where_syntology_ran_a_sample":404,"not_listed_spam_title":0,"listed":5676,"listed_where_code_ran":404,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":329,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":329,"listed_every_run_a_failure_of_syntologys_instrument":75,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/fairness","prev":"/task/fairness/papers/32","next":"/task/fairness/papers/34","papers":[{"url":null,"slug":"nonlinear-multi-objective-reinforcement","title":"Multi-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return","date":"2023-11-05","arxiv_id":"2311.02544","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-grasp-morality-in-concept","title":"LLMs grasp morality in concept","date":"2023-11-04","arxiv_id":"2311.02294","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-turing-mapping-the-landscape-of-llm","title":"Post Turing: Mapping the landscape of LLM Evaluation","date":"2023-11-03","arxiv_id":"2311.02049","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-fair-than-sorry-adversarial-missing","title":"Better Fair than Sorry: Adversarial Missing Data Imputation for Fair GNNs","date":"2023-11-02","arxiv_id":"2311.01591","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-fair-federated-learning-based-on","title":"Dynamic Fair Federated Learning Based on Reinforcement Learning","date":"2023-11-02","arxiv_id":"2311.00959","repositories_listed":0,"syntology":null},{"url":null,"slug":"responsible-emergent-multi-agent-behavior","title":"Responsible Emergent Multi-Agent Behavior","date":"2023-11-02","arxiv_id":"2311.01609","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairlabel-correcting-bias-in-labels","title":"FAIRLABEL: Correcting Bias in Labels","date":"2023-11-01","arxiv_id":"2311.00638","repositories_listed":0,"syntology":null},{"url":null,"slug":"loss-modeling-for-multi-annotator-datasets","title":"Noise Correction on Subjective Datasets","date":"2023-11-01","arxiv_id":"2311.00619","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-opportunities-of-green-computing-a","title":"On the Opportunities of Green Computing: A Survey","date":"2023-11-01","arxiv_id":"2311.00447","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-explicit-and-implicit-gender-bias","title":"Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation","date":"2023-11-01","arxiv_id":"2311.00306","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairwasp-fast-and-optimal-fair-wasserstein","title":"FairWASP: Fast and Optimal Fair Wasserstein Pre-processing","date":"2023-10-31","arxiv_id":"2311.00109","repositories_listed":0,"syntology":null},{"url":null,"slug":"parametric-fairness-with-statistical","title":"Parametric Fairness with Statistical Guarantees","date":"2023-10-31","arxiv_id":"2310.20508","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessment-of-differentially-private","title":"Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data","date":"2023-10-30","arxiv_id":"2310.19250","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-fair-metric-bridging-causality","title":"Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness","date":"2023-10-30","arxiv_id":"2310.19391","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-measuring-fairness-in-generative-models","title":"On Measuring Fairness in Generative Models","date":"2023-10-30","arxiv_id":"2310.19297","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-practical-non-adversarial","title":"Towards Practical Non-Adversarial Distribution Matching","date":"2023-10-30","arxiv_id":"2310.19690","repositories_listed":0,"syntology":null},{"url":null,"slug":"unmasking-bias-and-inequities-a-systematic","title":"Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models","date":"2023-10-30","arxiv_id":"2310.19917","repositories_listed":0,"syntology":null},{"url":null,"slug":"n-critics-self-refinement-of-large-language","title":"N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics","date":"2023-10-28","arxiv_id":"2310.18679","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-early-readouts-to-mediate-featural-bias","title":"Using Early Readouts to Mediate Featural Bias in Distillation","date":"2023-10-28","arxiv_id":"2310.18590","repositories_listed":0,"syntology":null},{"url":null,"slug":"wcld-curated-large-dataset-of-criminal-cases","title":"WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts","date":"2023-10-28","arxiv_id":"2310.18724","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-centric-online-market-for-machine","title":"Optimal Pricing for Data-Augmented AutoML Marketplaces","date":"2023-10-27","arxiv_id":"2310.17843","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-not-harm-protected-groups-in-debiasing","title":"Do Not Harm Protected Groups in Debiasing Language Representation Models","date":"2023-10-27","arxiv_id":"2310.18458","repositories_listed":0,"syntology":null},{"url":null,"slug":"prioritising-interactive-flows-in-data-center","title":"Prioritising Interactive Flows in Data Center Networks With Central Control","date":"2023-10-27","arxiv_id":"2402.00870","repositories_listed":0,"syntology":null},{"url":null,"slug":"proportional-fairness-in-clustering-a-social","title":"Proportional Fairness in Clustering: A Social Choice Perspective","date":"2023-10-27","arxiv_id":"2310.18162","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-sampled-split-learning-wssl","title":"Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments","date":"2023-10-27","arxiv_id":"2310.18479","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-fairness-for-predictions-using","title":"Counterfactual Fairness for Predictions using Generative Adversarial Networks","date":"2023-10-26","arxiv_id":"2310.17687","repositories_listed":0,"syntology":null},{"url":null,"slug":"lecardv2-a-large-scale-chinese-legal-case","title":"LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset","date":"2023-10-26","arxiv_id":"2310.17609","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-reasons-for-bias-an-argumentation","title":"Identifying Reasons for Bias: An Argumentation-Based Approach","date":"2023-10-25","arxiv_id":"2310.16506","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-interplay-between-fairness-and","title":"On the Interplay between Fairness and Explainability","date":"2023-10-25","arxiv_id":"2310.16607","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-responsible-machine-learning-datasets-with","title":"On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms","date":"2023-10-24","arxiv_id":"2310.15848","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-representation-learning-for-unified","title":"Robust Representation Learning for Unified Online Top-K Recommendation","date":"2023-10-24","arxiv_id":"2310.15492","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-canonical-data-transformation-for-achieving","title":"A Canonical Data Transformation for Achieving Inter- and Within-group Fairness","date":"2023-10-23","arxiv_id":"2310.15097","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-fairness-of-large-language","title":"Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications","date":"2023-10-23","arxiv_id":"2310.14607","repositories_listed":0,"syntology":null},{"url":null,"slug":"marginal-nodes-matter-towards-structure","title":"Marginal Nodes Matter: Towards Structure Fairness in Graphs","date":"2023-10-23","arxiv_id":"2310.14527","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-size-fits-all-observations-and","title":"\"One-Size-Fits-All\"? Examining Expectations around What Constitute \"Fair\" or \"Good\" NLG System Behaviors","date":"2023-10-23","arxiv_id":"2310.15398","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-optimal-graph-filter-design","title":"Fairness-aware Optimal Graph Filter Design","date":"2023-10-22","arxiv_id":"2310.14432","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributionally-robust-optimization-with-8","title":"Distributionally Robust Optimization with Bias and Variance Reduction","date":"2023-10-21","arxiv_id":"2310.13863","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-noma-assisted-otfs-isac-network-design","title":"Robust NOMA-assisted OTFS-ISAC Network Design with 3D Motion Prediction Topology","date":"2023-10-21","arxiv_id":"2310.13984","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-centric-multi-objective-learning","title":"A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems","date":"2023-10-20","arxiv_id":"2310.13260","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-and-contributing-factors-in-the","title":"Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)","date":"2023-10-20","arxiv_id":"2310.13343","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-inclusive-language-models-for","title":"She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models","date":"2023-10-20","arxiv_id":"2310.18333","repositories_listed":0,"syntology":null},{"url":null,"slug":"dissecting-causal-biases","title":"Dissecting Causal Biases","date":"2023-10-20","arxiv_id":"2310.13364","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairbranch-fairness-conflict-correction-on","title":"FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning","date":"2023-10-20","arxiv_id":"2310.13746","repositories_listed":0,"syntology":null},{"url":null,"slug":"feri-a-multitask-based-fairness-achieving","title":"FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation","date":"2023-10-20","arxiv_id":"2310.13820","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-theoretical-approach-to-characterize-the","title":"A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier","date":"2023-10-19","arxiv_id":"2310.12785","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-reweighting-of-distributions-an","title":"Constrained Reweighting of Distributions: an Optimal Transport Approach","date":"2023-10-19","arxiv_id":"2310.12447","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-graph-neural-networks-for-indian","title":"Exploring Graph Neural Networks for Indian Legal Judgment Prediction","date":"2023-10-19","arxiv_id":"2310.12800","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-browsing-models-for-linear-and-grid","title":"Unified Browsing Models for Linear and Grid Layouts","date":"2023-10-19","arxiv_id":"2310.12524","repositories_listed":0,"syntology":null},{"url":null,"slug":"auction-based-scheduling","title":"Auction-Based Scheduling","date":"2023-10-18","arxiv_id":"2310.11798","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairer-and-more-accurate-tabular-models","title":"Fairer and More Accurate Tabular Models Through NAS","date":"2023-10-18","arxiv_id":"2310.12145","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmic-robustness","title":"Algorithmic Robustness","date":"2023-10-17","arxiv_id":"2311.06275","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-identifiable-causal-representations-to","title":"From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling","date":"2023-10-17","arxiv_id":"2310.11011","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-the-effects-of-sex-related","title":"Studying the Effects of Sex-related Differences on Brain Age Prediction using brain MR Imaging","date":"2023-10-17","arxiv_id":"2310.11577","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-sponge-cake-dilemma-over-the-nile","title":"The Sponge Cake Dilemma over the Nile: Achieving Fairness in Resource Allocation with Cake Cutting Algorithms","date":"2023-10-16","arxiv_id":"2310.11472","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fair-and-calibrated-models","title":"Towards Fair and Calibrated Models","date":"2023-10-16","arxiv_id":"2310.10399","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-critical-survey-on-fairness-benefits-of-xai","title":"A Critical Survey on Fairness Benefits of Explainable AI","date":"2023-10-15","arxiv_id":"2310.13007","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-deep-learning-models-resistant-to-1","title":"Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning","date":"2023-10-15","arxiv_id":"2310.09891","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-collaborative-filtering-is-not","title":"When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations","date":"2023-10-15","arxiv_id":"2310.09687","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-model-agnostic-multi-group","title":"Efficient Model-Agnostic Multi-Group Equivariant Networks","date":"2023-10-14","arxiv_id":"2310.09675","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-and-examining-machine-learning","title":"Identifying and examining machine learning biases on Adult dataset","date":"2023-10-13","arxiv_id":"2310.09373","repositories_listed":0,"syntology":null},{"url":null,"slug":"im-not-racist-but-discovering-bias-in-the","title":"\"Im not Racist but...\": Discovering Bias in the Internal Knowledge of Large Language Models","date":"2023-10-13","arxiv_id":"2310.08780","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-to-gain-functional-transparency-in","title":"Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability","date":"2023-10-13","arxiv_id":"2310.08849","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-explanations-on-fairness-in","title":"The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features","date":"2023-10-12","arxiv_id":"2310.08617","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-stereotypical-biases-in-text-to","title":"Mitigating stereotypical biases in text to image generative systems","date":"2023-10-10","arxiv_id":"2310.06904","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-the-ethics-of-artificial","title":"A Review of the Ethics of Artificial Intelligence and its Applications in the United States","date":"2023-10-09","arxiv_id":"2310.05751","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-classifiers-that-abstain-without-harm","title":"Fair Classifiers that Abstain without Harm","date":"2023-10-09","arxiv_id":"2310.06205","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-models-meet-visualizations","title":"Foundation Models Meet Visualizations: Challenges and Opportunities","date":"2023-10-09","arxiv_id":"2310.05771","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-prediction-modelers-and-decision-makers","title":"On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model","date":"2023-10-09","arxiv_id":"2310.05598","repositories_listed":0,"syntology":null},{"url":null,"slug":"resolving-the-imbalance-issue-in-hierarchical","title":"Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation","date":"2023-10-09","arxiv_id":"2310.05318","repositories_listed":0,"syntology":null},{"url":null,"slug":"unleashing-the-power-of-neural-collapse-for","title":"Unleashing the power of Neural Collapse for Transferability Estimation","date":"2023-10-09","arxiv_id":"2310.05754","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-feature-importance-scores-for","title":"Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates","date":"2023-10-06","arxiv_id":"2310.04352","repositories_listed":0,"syntology":null},{"url":null,"slug":"nash-welfare-and-facility-location","title":"Nash Welfare and Facility Location","date":"2023-10-06","arxiv_id":"2310.04102","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-equal-opportunity-on","title":"Interventions Against Machine-Assisted Statistical Discrimination","date":"2023-10-06","arxiv_id":"2310.04585","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimarginal-generative-modeling-with","title":"Multimarginal generative modeling with stochastic interpolants","date":"2023-10-05","arxiv_id":"2310.03695","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-fairness-for-human-ai","title":"Rethinking Fairness for Human-AI Collaboration","date":"2023-10-05","arxiv_id":"2310.03647","repositories_listed":0,"syntology":null},{"url":null,"slug":"capcodre-a-computational-systems-biology-and","title":"Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches","date":"2023-10-04","arxiv_id":"2311.09229","repositories_listed":0,"syntology":null},{"url":null,"slug":"digital-ethics-in-federated-learning","title":"Digital Ethics in Federated Learning","date":"2023-10-04","arxiv_id":"2310.03178","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-feature-selection-a-comparison-of-multi","title":"Fair Feature Selection: A Comparison of Multi-Objective Genetic Algorithms","date":"2023-10-04","arxiv_id":"2310.02752","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-enhancing-mixed-effects-deep","title":"Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data","date":"2023-10-04","arxiv_id":"2310.03146","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-accuracy-a-review-on-diversity","title":"Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks","date":"2023-10-03","arxiv_id":"2310.02294","repositories_listed":0,"syntology":null},{"url":null,"slug":"eye-fairness-a-large-scale-3d-imaging-dataset","title":"FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling","date":"2023-10-03","arxiv_id":"2310.02492","repositories_listed":0,"syntology":null},{"url":null,"slug":"nash-regret-guarantees-for-linear-bandits","title":"Nash Regret Guarantees for Linear Bandits","date":"2023-10-03","arxiv_id":"2310.02023","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-based-scheme-for-fair-timeliness","title":"A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks","date":"2023-10-02","arxiv_id":"2310.01396","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-and-implementing-conventional","title":"Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features","date":"2023-10-02","arxiv_id":"2310.01679","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-brief-history-of-prompt-leveraging-language","title":"A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)","date":"2023-09-30","arxiv_id":"2310.04438","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-operationalizing-pipeline-aware-ml","title":"Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools","date":"2023-09-29","arxiv_id":"2309.17337","repositories_listed":0,"syntology":null},{"url":null,"slug":"constant-approximation-for-individual","title":"Constant Approximation for Individual Preference Stable Clustering","date":"2023-09-28","arxiv_id":"2309.16840","repositories_listed":0,"syntology":null},{"url":null,"slug":"effl-egalitarian-fairness-in-federated","title":"Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect","date":"2023-09-28","arxiv_id":"2309.16338","repositories_listed":0,"syntology":null},{"url":null,"slug":"max-sliced-mutual-information","title":"Max-Sliced Mutual Information","date":"2023-09-28","arxiv_id":"2309.16200","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-poisoning-fair-representations","title":"Towards Poisoning Fair Representations","date":"2023-09-28","arxiv_id":"2309.16487","repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-multi-objective-hyperparameter","title":"Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives","date":"2023-09-26","arxiv_id":"2309.14936","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-bias-in-algorithmic-hiring","title":"Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey","date":"2023-09-25","arxiv_id":"2309.13933","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictable-performance-bias-in-unsupervised","title":"(Predictable) Performance Bias in Unsupervised Anomaly Detection","date":"2023-09-25","arxiv_id":"2309.14198","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-principles-of-robust-multi-armed","title":"Design Principles of Robust Multi-Armed Bandit Framework in Video Recommendations","date":"2023-09-24","arxiv_id":"2310.01419","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-of-social-bias-in-vision-language","title":"Survey of Social Bias in Vision-Language Models","date":"2023-09-24","arxiv_id":"2309.14381","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-fairness-age-harmless-parkinson-s","title":"Beyond Fairness: Age-Harmless Parkinson's Detection via Voice","date":"2023-09-23","arxiv_id":"2309.13292","repositories_listed":0,"syntology":null},{"url":null,"slug":"penalties-and-rewards-for-fair-learning-in","title":"Penalties and Rewards for Fair Learning in Paired Kidney Exchange Programs","date":"2023-09-23","arxiv_id":"2309.13421","repositories_listed":0,"syntology":null},{"url":null,"slug":"faircomp-workshop-on-fairness-and-robustness","title":"FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing","date":"2023-09-22","arxiv_id":"2309.12877","repositories_listed":0,"syntology":null},{"url":null,"slug":"pursuing-counterfactual-fairness-via","title":"Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments","date":"2023-09-22","arxiv_id":"2309.13005","repositories_listed":0,"syntology":null},{"url":null,"slug":"dr-fermi-a-stochastic-distributionally-robust","title":"Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework","date":"2023-09-20","arxiv_id":"2309.11682","repositories_listed":0,"syntology":null}],"record_sha256":"9ecec689d1517c87d5e378cf1cada5ad5aa7ffd45d6193918cf8929b30357892","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}