Shapley Values
PulseAugur coverage of Shapley Values — every cluster mentioning Shapley Values across labs, papers, and developer communities, ranked by signal.
8 day(s) with sentiment data
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New Shapley-inspired k-means algorithm enhances feature weighting
Researchers have developed SHARK (Shapley Reweighted k-means), a novel feature-weighting method for clustering algorithms that avoids the need for additional hyperparameter tuning. This approach leverages Shapley values…
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New Priority-Aware Shapley Value method enhances AI data valuation
Researchers have introduced Priority-Aware Shapley Value (PASV), a novel method for data valuation and feature attribution that addresses the limitations of traditional Shapley values. PASV incorporates precedence const…
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New RuleSHAP framework enhances ML inference for epidemiological data
Researchers have developed a new framework called RuleSHAP to improve statistical inference for machine learning models in epidemiology. This framework integrates Bayesian regression, tree ensembles, and Shapley values …
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New framework decodes modality contributions in audio-visual speech recognition
Researchers have developed Dr. SHAP-AV, a framework utilizing Shapley values to analyze how audio-visual speech recognition models balance acoustic and visual information. Experiments across six models and varying noise…
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New Shapley Value method explains multimodal AI models
Researchers have developed a novel extension of Shapley Values to explain the behavior of multimodal multilingual models (MLLMs). This framework addresses the challenges of integrating text and audio data by treating th…
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New Aumann-SHAP framework explains ML decisions via counterfactual geometry
Researchers have developed Aumann-SHAP, a new framework for explaining machine learning model decisions by analyzing counterfactual interactions. This method decomposes changes by focusing on a local hypercube between b…
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Shapley compositions offer new method for multiclass AI prediction explanation
Researchers have introduced a novel method called Shapley compositions to explain probabilistic predictions in multiclass machine learning models. This approach extends the traditional Shapley value concept, which is ty…
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New FedMTFI architecture boosts federated learning accuracy
Researchers have introduced FedMTFI, a new architecture designed to improve federated learning in heterogeneous environments. This approach clusters clients based on similar hardware and model types, allowing each clust…
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New methods improve Shapley value approximation for ML attribution
Researchers have developed new methods for approximating Shapley values, a crucial metric for attribution in machine learning. Two papers introduce novel algorithms, Adalina and ShaplEIG, that improve efficiency and acc…
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New MVP-Shapley Framework Evaluates Player Contributions Using Shapley Values
Researchers have developed a new framework called MVP-Shapley to evaluate the Most Valuable Player (MVP) in basketball, particularly for esports and online gaming contexts. This method utilizes play-by-play data to proc…
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New research advances graph representation learning and Shapley value computation
Researchers are developing advanced methods for graph representation learning, focusing on improving generalization and efficiency. New models like SPG aim to parse spectral responses and use prototype-guided propagatio…
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New method combats false-name manipulation in ML data attribution
Researchers have developed a new data attribution method called the quotient semivalue mechanism to combat false-name manipulation in machine learning. This approach addresses issues where contributors might inflate the…
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QuadraSHAP offers stable, scalable Shapley values for product games
Researchers have developed QuadraSHAP, a novel method for efficiently calculating Shapley values in product games, which are common in machine learning explainability. The technique reduces the complex calculation to a …
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Quadrature-TreeSHAP offers faster, more stable AI model explanations
Researchers have developed Quadrature-TreeSHAP, a novel method for explaining tree ensemble predictions that is depth-independent and more numerically stable than existing approaches. This new technique extends naturall…
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Researchers develop stable, explainable AI for elderly fall detection
Researchers have developed a new framework for skeleton-based fall detection that uses a temporally stabilized attribution mechanism called T-SHAP. This method enhances the interpretability of AI models used in elderly …
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New statistical viewpoint improves probabilistic value estimation for AI
Researchers have developed a new statistical viewpoint for understanding and improving probabilistic value estimation methods. Their work identifies a common first-order error structure across existing estimators, which…
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New framework uses K-Shapley values for meritocratic fairness in bandits
Researchers have introduced a novel framework for achieving meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback. This new approach extends the Shapley value concept to a K-Shapl…
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Researchers develop Shapley value explainers for temporal graph neural networks
Researchers have developed two new model-agnostic explainers for Temporal Graph Neural Networks (TGNNs), utilizing Shapley and Owen values. These methods aim to make the predictions of TGNNs, which combine spatial and t…
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Study finds Shapley value benchmarks for AI explainability misaligned with human utility
A new paper examines the evaluation of explainable AI (XAI) methods, specifically Shapley value variants, in high-stakes scenarios like fraud detection. Researchers found that standard quantitative metrics for XAI do no…