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..
Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s)
Authors: Diyang Li, Charles Ling, zhiqiang xu, Huan Xiong, Bin Gu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Encouraging results are exhibited on real-world benchmarks. |
| Researcher Affiliation | Academia | Diyang Li1, Charles X. Ling2, Zhiqiang Xu3, Huan Xiong3 & Bin Gu3, 1Cornell University 2Western University 3Mohamed bin Zayed University of Artificial Intelligence |
| Pseudocode | Yes | Algorithm 1 SAGO Algorithm |
| Open Source Code | Yes | To ensure the replicability, Python codes corresponding to the pivotal components of the proposed algorithms are incorporated within the supplementary materials. |
| Open Datasets | Yes | Dataset We employ real-world datasets from Open ML (Vanschoren et al., 2014) and UCI repository (Asuncion & Newman, 2007) for our simulations. |
| Dataset Splits | Yes | We randomly partition the datasets into training, validation, and testing sets, with 70%, 15%, and 15% of the total samples, respectively. |
| Hardware Specification | Yes | All experiments presented in this study were conducted on a workstation running the Ubuntu 18.04 operating system, equipped with Intel 2.30GHz CPU 200 and 400.0GB of RAM. |
| Software Dependencies | No | The paper mentions 'Python 3.7' as the implementation language and libraries like 'Num Py and Sci Py', 'Scikit-learn', and 'Hyperopt' without specifying version numbers for these libraries. |
| Experiment Setup | Yes | The parameterizers are set to µ (t) =4t2, ν (s) = 1 s2, respectively. ... The convergence tolerance ε for batch training is 1e-7 and the tolerance ϵ for hyperparameter in the outer-level problem is 1e-4. |