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..
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Authors: Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As a first step, we provide a simple construction that rules out the existence of a distribution-independent implicit regularizer that governs the generalization ability of SGD. We then demonstrate a learning problem that rules out a very general class of distribution-dependent implicit regularizers from explaining generalization, which includes strongly convex regularizers as well as non-degenerate norm-based regularizations. |
| Researcher Affiliation | Collaboration | Assaf Dauber Department of Electrical Engineering Tel-Aviv University EMAIL Meir Feder Department of Electrical Engineering Tel-Aviv University EMAIL Tomer Koren School of CS, Tel Aviv University & Google Research Tel Aviv EMAIL Roi Livni Department of Electrical Engineering Tel Aviv University EMAIL |
| Pseudocode | No | The paper describes algorithms (SGD, GD) using mathematical equations and textual descriptions, but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | No | The paper presents theoretical constructions and does not use or refer to publicly available datasets in the context of empirical training and evaluation. |
| Dataset Splits | No | The paper is theoretical and focuses on mathematical constructions and proofs, therefore it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper includes simulations but does not provide specific details about the hardware used (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers required to replicate any part of the work. |
| Experiment Setup | Yes | Figure 1: Simulation of GD (with step size η = 0.2) on 푓퐴,Σ for θ = 1 and varying values of 푏. We see that GD does not necessarily converge to the nearest solution, and tuning 푏changes the point towards which it is biased. |