Aug 30, 2023 · This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms.

Jun 25, 2024 · This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA ...

This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In ...

This paper demonstrates how a surrogate-based auto- tuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In ...

Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems. Y Cho, JW Demmel, M Dereziński, H Li, H Luo, MW Mahoney, RJ Murray.

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs ... Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems.

Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems ... Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known ...

This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In ...

This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In ...

We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets ...