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..
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality
Authors: Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the above result empirically, we simulated Ī“t in (6) throughout all iterations of Algorithms 1 for different sets of parameters and presented the results in Figure 1 along with their corresponding convergence rates given in Corollary 1. As it is shown in these figure, the above convergence rates correctly capture the behaviour of the dynamics in (6). |
| Researcher Affiliation | Academia | Ilyas Fatkhullin ETH AI Center & ETH Zurich Jalal Etesami* EPFL Niao He ETH Zurich Negar Kiyavash EPFL |
| Pseudocode | Yes | Algorithm 1: SGD with restarts ... Algorithm 2: PAGER (PAGE with restarts) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code. |
| Open Datasets | No | The paper focuses on theoretical analysis and simulations of dynamics, not empirical evaluation on specific, publicly available datasets. No dataset names or access information are provided. |
| Dataset Splits | No | The paper does not describe experiments on datasets with specified training/test/validation splits. It refers to simulations of theoretical dynamics. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for any computational work or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper describes parameters used for simulating theoretical dynamics (e.g., h(t) = tĪ², Ļ(t) = 2Āµ t1/Ī±, Ļ values for Figure 1) but does not provide specific experimental setup details like hyperparameters, optimizers, or training configurations for machine learning models or real-world data experiments. |