Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers
Authors: Jyotikrishna Dass, Rabi Mahapatra
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform thorough empirical analysis with large synthetic and real datasets to evaluate the performance of Householder sketch and compare with (Maalouf et al., 2019). |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA. Correspondence to: Jyotikrishna Dass <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 HOUSEHOLDER-SKETCH(X, y); see Theorem 2.2 |
| Open Source Code | Yes | We have open-sourced our codes here. |
| Open Datasets | Yes | We used following datasets for evaluation, and fair comparison of LMS-QR performance with the default LMS solvers (with cross validation), and with Fast Caratheodory coreset based LMS-BOOST (Maalouf et al., 2019). (i) Synthetic data (X, y) comprising uniform random entries in [0, 100) for sequential experiments. (ii) 3D Road network(Kaul et al., 2013) dataset with n = 434, 874 data samples. (iii) Individual household electric power consumption (Hebrail & Berard, 2012) dataset with n = 2, 075, 259 data samples. |
| Dataset Splits | Yes | Cross validation folds, |m|= 3 for synthetic datasets (a)-(j) and |m|= 2 for real datasets (k)-(l) |
| Hardware Specification | Yes | We used Google Colab to run our experiments with the above LMS-QR algorithms via Python3 Google Compute Engine running on Intel Xeon CPU @ 2.20GHz and 25 GB RAM. ... For distributed experiments, we used the Anaconda Python distribution and MPI for Python (mpi4py) package on the Texas A&M University HPRC Ada computing cluster of Intel Xeon CPU @ 2.5GHz. |
| Software Dependencies | No | To implement Algorithm 1, we use LAPACK.dgeqrf(), and LAPACK.dormqr() subroutines for HOUSEHOLDER-QR, and MULTIPLY-QC, respectively. ... We used Google Colab to run our experiments with the above LMS-QR algorithms via Python3 ... We used following datasets for evaluation, and fair comparison of LMS-QR performance with the default LMS solvers (with cross validation), and with Fast Caratheodory coreset based LMS-BOOST (Maalouf et al., 2019). ... For distributed experiments, we used the Anaconda Python distribution and MPI for Python (mpi4py) package... Linear algebra was handled by LAPACK/BLAS, through the Intel Math Kernel Library. |
| Experiment Setup | Yes | For various size of hyper-parameter set for cross validation, |A|= {50, 100, 200, 300}, for cross validation, Figure 1 (g)-(i) depict LMS-QR to be consistently faster than LMS-CV and LMS-BOOST. We observe similar trend for the real datasets in Figure 1 (k)-(l). ... Each test was performed 20 times, and the best result was chosen. |