Li et al., 2024 - Google Patents
Faster approximation of probabilistic and distributional values via least squaresLi et al., 2024
View PDF- Document ID
- 8319498597337361413
- Author
- Li W
- Yu Y
- Publication year
- Publication venue
- Proceedings of the International Conference on Learning Representations (ICLR)
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Snippet
The family of probabilistic values, axiomatically-grounded in cooperative game theory, has recently received much attention in data valuation. However, it is often computationally expensive to compute exactly (exponential wrt the number of data to valuate denoted by n) …
- 238000011156 evaluation 0 abstract description 35
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- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
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- G06F17/30533—Other types of queries
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- G—PHYSICS
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- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
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- G06K9/62—Methods or arrangements for recognition using electronic means
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- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6296—Graphical models, e.g. Bayesian networks
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