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..
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning
Authors: Thomas Tck Zhang, Behrad Moniri, Ansh Nagwekar, Faraz Rahman, Anton Xue, Hamed Hassani, Nikolai Matni
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate theoretically and numerically that this suboptimality is fundamental, and that layer-wise preconditioning emerges naturally as the solution. [...] Lastly, we carefully numerically verify our theoretical predictions. Notably, we confirm the findings in Benzing (2022) that full second-order methods heavily underperform KFAC in convergence rate and stability. We also show standard tools like Adam-like preconditioning and batch-norm (Ioffe & Szegedy, 2015) do not fix the issues we identify, even for our simple models, and may even hurt generalization in the latter s case. |
| Researcher Affiliation | Academia | 1University of Pennsylvania. Correspondence to: T. Zhang, B. Moniri <EMAIL>. |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. While algorithmic steps are described mathematically (e.g., equations 5 and 8), they are not presented in a structured pseudocode format. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes data generation processes for its experiments, for example: "Our data generation process for the training task and the transfer task are as follows: ys i = Fs G xs i + εs i, xs i i.i.d. Σ1/2 x,s Unif({ 1}d X), εs i i.i.d. N(0, σ2 ε,s Id Y), s {test, train}". It uses synthetic data generated according to specified distributions rather than pre-existing public datasets, and no access to the generated data is provided. |
| Dataset Splits | No | The paper describes generating data for training and transfer tasks separately, but does not provide explicit training/validation/test splits (e.g., percentages or counts) of a single static dataset. The data is generated on-the-fly for specific tasks. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation of the experiments. |
| Experiment Setup | Yes | Our data generation process for the training task and the transfer task are as follows: ... We use d X = 100, d Y = 15, k = 8, and batch size n = 1024. ... We use the same learning rate 10 2 for each optimizer except for NGD, in which we used 10 4. The batch size is 1024. ... In this experiment, we set d X = 200, n = 6000, and dh = 1000 and set λG 0. ... We set d X = 900, n = 5000, dh = 1000, and Σx = Σ(0.5) x . |