The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
Authors: Roi Livni
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 rlivni@tauex.tau.ac.il |
| 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. |