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 [1].
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. |