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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization
Authors: Milad Sefidgaran, Kimia Nadjahi, Abdellatif Zaidi
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 56,402 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,904 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is a theoretical paper with rigorously proven claims, and does not involve any experiment. |
| Researcher Affiliation | Collaboration | 1 Paris Research Center, Huawei Technologies France 2 CNRS, ENS Paris, France 3 Universit e Gustave Eiffel, France |
| Pseudocode | No | The considered noisy iterative optimization algorithm consists of the following steps: (Initialization) Sample Θ RD d and set the initial model s parameters to W0 = ΘW 0, where W 0 Rd. (Iterate) For t [T], apply the update rule W t = Proj n W t 1 ηt w b R(Vt, ΘW t 1) + σtεt o , (17) with ηt > 0 (the learning rate), σt 0 (the variance of the Gaussian noise), and εt N(0d, Id) (the isotropic Gaussian noise). Here, the projection is an optional operator often used to keep the norm of the model parameters bounded. (Output) Return the final hypothesis WT = ΘW T . |
| Open Source Code | No | Our work does not involve any experiment. |
| Open Datasets | No | Our work is a theoretical paper with rigorously proven claims, and does not involve any experiment. |
| Dataset Splits | No | Our work does not involve any experiment. |
| Hardware Specification | No | Our work does not involve any experiment. |
| Software Dependencies | No | Our work does not involve any experiment. |
| Experiment Setup | No | Our work does not involve any experiment. |