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Found 8 papers, 4 papers with code
Efficient Learning of Interpretable Classification Rules
The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable.
MLIC: A MaxSAT-Based framework for learning interpretable classification rules
The wide adoption of machine learning approaches in the industry, government, medicine and science has renewed the interest in interpretable machine learning: many decisions are too important to be delegated to black-box techniques such as deep neural networks or kernel SVMs.
Quantifying homologous proteins and proteoforms
Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms.
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations
Computation of the trace of a matrix function plays an important role in many scientific computing applications, including applications in machine learning, computational physics (e. g., lattice quantum chromodynamics), network analysis and computational biology (e. g., protein folding), just to name a few application areas.
Data Structures and Algorithms
Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions
Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partition functions of discrete graphical models, minimum-volume ellipsoids, metric learning and kernel learning.
Convex Total Least Squares
The special case when all dependent and independent variables have the same level of uncorrelated Gaussian noise, known as ordinary TLS, can be solved by singular value decomposition (SVD).
Iterative Log Thresholding
Sparse reconstruction approaches using the re-weighted l1-penalty have been shown, both empirically and theoretically, to provide a significant improvement in recovering sparse signals in comparison to the l1-relaxation.
Linear programming analysis of loopy belief propagation for weighted matching
Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees.
