REVIEW 3 cited by
The Shapley Value in Machine Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fundamental concepts of cooperative game theory and axiomatic properties of the Shapley value. Then we give an overview of the most important applications of the Shapley value in machine learning: feature selection, explainability, multi-agent reinforcement learning, ensemble pruning, and data valuation. We examine the most crucial limitations of the Shapley value and point out directions for future research.
Forward citations
Cited by 3 Pith papers
-
Learning Minimal Representations of Fermionic Ground States
Autoencoders trained on Hubbard ground-state measurement vectors show a sharp reconstruction threshold at L−1 latent dimensions, and the decoder can be used as a variational ansatz for energy minimization.
-
Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components
Probabilistic attribute embeddings derived from countermeasure embeddings match raw embedding performance on spoofed speech detection and attack attribution while providing component-level explanations.
-
Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
A multi-round combination of Quality Inference and group-testing FedGT detects misbehaving clients and evaluates client contributions under secure aggregation, outperforming the original schemes on cross-silo benchmarks.
Discussion (0). Continue with ORCID to comment.