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Found 5 papers, 2 papers with code
Ridge Regression with Frequent Directions: Statistical and Optimization Perspectives
Despite its impressive theory \& practical performance, Frequent Directions (\acrshort{fd}) has not been widely adopted for large-scale regression tasks.
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams
Anomaly detection at scale is an extremely challenging problem of great practicality.
Similarity of Neural Networks with Gradients
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights on further theoretical analysis and empirical studies.
Iterative Hessian Sketch in Input Sparsity Time
Scalable algorithms to solve optimization and regression tasks even approximately, are needed to work with large datasets.
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Work on approximate linear algebra has led to efficient distributed and streaming algorithms for problems such as approximate matrix multiplication, low rank approximation, and regression, primarily for the Euclidean norm $\ell_2$.
