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..
Generalization of noisy SGD in unbounded non-convex settings
Authors: Leello Tadesse Dadi, Volkan Cevher
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Formally, we establish time-independent information theoretic generalization bounds for Stochastic Gradient Langevin Dynamics (SGLD) that do not diverge as the iteration count increases. Our bounds are obtained through a stability argument: we analyze the difference between two SGLD sequences ran in parallel on two datasets sampled from the same distribution. Our result only requires an isoperimetric inequality to hold, which is merely a restriction on the tails of the loss. |
| Researcher Affiliation | Academia | 1EPFL, Lausanne, Switzerland. Correspondence to: Leello Dadi <EMAIL>. |
| Pseudocode | No | The paper describes the SGLD recursion as "Xk+1 = Xk ηg(Xk, Bk) + r2η β Nk+1 (SGLD)" but does not present a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper defines the empirical approximation Fn given by Fn(x, D) = 1 n Pn i=1 f(x, Zi) based on a dataset D of n independent, identically distributed samples D = Z1, . . . , Zn from ν, but does not use or provide access to any specific dataset for experiments. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with specific datasets, therefore it does not mention training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical analysis, thus it does not specify any software dependencies or versions. |
| Experiment Setup | No | The paper focuses on theoretical analysis and derivation of generalization bounds, and therefore does not include details about an experimental setup, hyperparameters, or system-level training settings. |