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..
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
Authors: Yuze Ge, Rujun Jiang
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real datasets show that our algorithm outperforms existing ones in most cases, both in terms of runtime and sample complexity. |
| Researcher Affiliation | Academia | Yuze Ge & Rujun Jiang School of Data Science, Fudan University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 SOREL |
| Open Source Code | Yes | The source code is available at https://github.com/SXFXuz/SOREL. |
| Open Datasets | Yes | Five tabular regression benchmarks are used for the least squares loss: yacht (Tsanas & Xifara, 2012), energy (Baressi Šegota et al., 2019), concrete (Yeh, 2006), kin8nm (Akujuobi & Zhang, 2017), power (Tüfekci, 2014). |
| Dataset Splits | Yes | We split the training set and test set in a 4:1 ratio and used five-fold cross-validation to report the average results on the test set. |
| Hardware Specification | Yes | We run all experiments on a laptop with 16.0 GB RAM and Intel i7-1360P 2.20 GHz CPU. |
| Software Dependencies | No | The paper states: "All algorithms are implemented in Python 3.8." This provides a programming language and its version but does not list any specific versioned libraries or solvers used, which is required for a 'Yes' classification. |
| Experiment Setup | Yes | For the selection of step size α, we set the random seed s {1, . . . , S}. For a single seed s, we calculate the average training loss of the last ten epochs, donated by Ls(α). We choose α that minimizes 1/S PS s=1 Ls(α), where α {1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1}. For LSVRG, we set the length of an epoch to n. For SOREL, we set Tk = mk = n. Moreover, we set batch size to 64 for all algorithms with mini-batching. For SOREL, we follow the parameter values given in Theorem 1. In particular, we set θk = k/(k+1) and τk = 20n/(k+1) in all experiments. Therefore, there are only two parameters α and ηk left to tune. We set ηk = C/(k+1)n and choose C from {1e-2, 2e-2, 4e-2, 1e-1, 2e-1, 4e-1, 1e0, 2e0, 4e0, 1e1}... |