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Found 4 papers, 1 papers with code
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
By extending our approach to block-design matrices where one block is a Kronecker product, we also achieve subquadratic-time algorithms for (1) Kronecker ridge regression and (2) updating the factor matrices of a Tucker decomposition in ALS, which is not a pure Kronecker regression problem, thereby improving the running time of all steps of Tucker ALS.
Fast Low-Rank Tensor Decomposition by Ridge Leverage Score Sampling
Low-rank tensor decomposition generalizes low-rank matrix approximation and is a powerful technique for discovering low-dimensional structure in high-dimensional data.
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
In this paper, we aim to develop LSH schemes for distance functions that measure the distance between two probability distributions, particularly for f-divergences as well as a generalization to capture mutual information loss.
Categorical Feature Compression via Submodular Optimization
To achieve this, we introduce a novel re-parametrization of the mutual information objective, which we prove is submodular, and design a data structure to query the submodular function in amortized $O(\log n )$ time (where $n$ is the input vocabulary size).
