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Efficient Data Representation by Selecting Prototypes with Importance Weights

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-68874-4openalex-snapshot
Mathematical Programming the State of the Art: Bonn 1982
inferredcites →https://doi.org/10.1007/978-3-642-68874-4_10openalex-snapshot
Submodular functions and convexity
inferredcites →https://doi.org/10.1007/bf01588971openalex-snapshot
An analysis of approximations for maximizing submodular set functions—I
inferredcites →https://doi.org/10.1007/s10115-016-0976-2openalex-snapshot
Supervised item response models for informative prediction
inferredcites →https://doi.org/10.1007/s10994-015-5540-xopenalex-snapshot
Tikhonov, Ivanov and Morozov regularization for support vector machine learning
inferredcites →https://doi.org/10.1080/10618600.2018.1473777openalex-snapshot
Algorithms for Fitting the Constrained Lasso
inferredcites →https://doi.org/10.1115/1.3424255openalex-snapshot
The Theory of Approximate Methods and Their Application to the Numerical Solution of Singular Integral Equations
inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot
LIBSVM
inferredcites →https://doi.org/10.1145/2783258.2788613openalex-snapshot
Intelligible Models for HealthCare
inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot
"Why Should I Trust You?"
inferredcites →https://doi.org/10.1145/358557.358577openalex-snapshot
Programmers use slices when debugging
inferredcites →https://doi.org/10.1214/11-aoas495openalex-snapshot
Prototype selection for interpretable classification
inferredcites →https://doi.org/10.1214/17-aos1679openalex-snapshot
Restricted strong convexity implies weak submodularity
inferredcites →https://doi.org/10.18653/v1/n16-3020openalex-snapshot
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
inferredcites →https://doi.org/10.48550/arxiv.1102.3975openalex-snapshot
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
inferredcites →https://doi.org/10.48550/arxiv.1606.05798openalex-snapshot
Interpretable Two-level Boolean Rule Learning for Classification
inferredcites →https://doi.org/10.48550/arxiv.1611.01511openalex-snapshot
Algorithms for Fitting the Constrained Lasso
inferredcites →https://doi.org/10.48550/arxiv.1703.02723openalex-snapshot
Scalable Greedy Feature Selection via Weak Submodularity
inferredcites →https://doi.org/10.48550/arxiv.1703.04730openalex-snapshot
Understanding Black-box Predictions via Influence Functions
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0048openalex-snapshot
Support Vector Machines on a Budget
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0069openalex-snapshot
A Kernel Method for the Two-Sample-Problem

What points at this work

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