SOTAVerified

Fairness

Papers

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Showing 1601–1625 of 5676 papers

TitleStatusHype
Efficient candidate screening under multiple tests and implications for fairness—0
Ensuring Fair LLM Serving Amid Diverse Applications—0
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms—0
Efficient Device Scheduling with Multi-Job Federated Learning—0
Efficient DNN-Powered Software with Fair Sparse Models—0
Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy—0
Efficient Fairness-Performance Pareto Front Computation—0
Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection—0
Efficient Fair Principal Component Analysis—0
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance—0
Convex Fairness Constrained Model Using Causal Effect Estimators—0
Efficient Gender Debiasing of Pre-trained Indic Language Models—0
Efficient k-means with Individual Fairness via Exponential Tilting—0
Converging Radar and Communications in the Superposition Transmission—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Efficient Major Transition Exchange under Distributional and Dual Priority-respecting Constraints—0
A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View—0
Efficient Model-Agnostic Multi-Group Equivariant Networks—0
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
Efficient Multi-Task Inferencing with a Shared Backbone and Lightweight Task-Specific Adapters for Automatic Scoring—0
A Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends—0
An Efficient Framework for Crediting Data Contributors of Diffusion Models—0
Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints—0
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect—0
AI Data Development: A Scorecard for the System Card Framework—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.86—Unverified
21D-CSNNPredictive Equality (age)97.8—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)96.87—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.97—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.45—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.68—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.31—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)6.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)1.96—Unverified