Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
Authors: Roi Livni
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 28,377 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,880 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 | We analyze the sample complexity of full-batch Gradient Descent (GD) in the setup of non-smooth Stochastic Convex Optimization. We show that the generalization error of GD... Our bound follows from a new generalization bound... This resolves an open problem... The proof is an immediate corollary of Lemmas 8 and 9, which we prove in Appendices A.1 and A.2 respectively. |
| Researcher Affiliation | Academia | Roi Livni School of Electrical Engineering Tel Aviv University EMAIL |
| Pseudocode | No | GD depends on hyperparameters π βand Ξ· 0 and operates as follows on the empirical risk. The algorithm receives as input a sample π= {π§1, . . . , π§π}, defines π€0 = 0, and operates for πiterations according to the following recursion: π€π‘+1 = Ξ π€π‘ Ξ·/π ππ π‘=1 π€π‘, (4) |
| Open Source Code | No | No mention of open-source code for the methodology or any code release. |
| Open Datasets | No | No specific datasets, public or otherwise, are mentioned for empirical evaluation. The paper works with abstract samples drawn from a distribution. |
| Dataset Splits | No | No empirical validation process or dataset splits are described. |
| Hardware Specification | No | No hardware specifications are mentioned as no experiments were conducted. |
| Software Dependencies | No | No software dependencies are mentioned as no experiments were conducted. |
| Experiment Setup | No | No experimental setup details, hyperparameters, or training configurations are described, as no experiments were conducted. |