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..
Analysis of stochastic Lanczos quadrature for spectrum approximation
Authors: Tyler Chen, Thomas Trogdon, Shashanka Ubaru
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The quality of our bounds is demonstrated using numerical experiments. 5. Numerical verification and discussion |
| Researcher Affiliation | Collaboration | 1Department of Applied Mathematics, University of Washington, Seattle, Washington, USA 2IBM T.J. Watson Research Center, Yorktown Heights, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Stochastic Lanczos Quadrature and Algorithm 2 Lanczos |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | resnet20 is a Hessian for the ResNet20 network (He et al., 2016) trained on the Cifar-10 dataset. The California and Erdos992 examples are graph adjacency matrices from the sparse matrix suite (Davis & Hu, 2011) and the MNIST cov example is the covariance matrix of the MNIST training data |
| Dataset Splits | No | The paper mentions using standard datasets like CIFAR-10 and MNIST but does not provide specific details on how these datasets were split into training, validation, or test sets for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Py Hessian (Yao et al., 2020)' but does not provide specific version numbers for this or any other key software dependencies required for replication. |
| Experiment Setup | Yes | In Figure 2 for several test problems. Qualitatively, we observe several types of behavior in both the true Wasserstein distance and the bounds. From left to right, nv = 2, 6, 9, 68, 11 chosen so that nv is roughly of size O(n 1). Legend: d W(Φ[A], [Ψi]gq k ( ), bound Pn j=0 max{[di]j, [di]j+1}([θi]j+1 θi]j) ( ), bound 12I[A](2k 1) 1 ( ), (Φ(d ) Φ(c)) |d c| described in (2) ( ), I[A]n 1 ( ). |