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..
The Implicit Bias of Gradient Descent on Separable Multiclass Data
Authors: Hrithik Ravi, Clay Scott, Daniel Soudry, Yutong Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Appendix I, we show experimental results demonstrating implicit bias towards the hard margin SVM when using the Pair Log Loss, in line with Theorem 3.4. |
| Researcher Affiliation | Academia | 1University of Michigan 2Technion Israel Institute of Technology 3Illinois Institute of Technology |
| Pseudocode | No | The paper presents theoretical proofs and mathematical derivations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for recreating the figures can be found at https://github.com/Yutong Wang ML/neurips2024-multiclass-IR-figures |
| Open Datasets | Yes | We verify this experimentally in the Python notebook checking_conjecture_in_Appendix_H.ipynb available at https://github.com/Yutong Wang ML/neurips2024-multiclass-IR-figures. The code for recreating the figures can be found at https://github.com/Yutong Wang ML/neurips2024-multiclass-IR-figures |
| Dataset Splits | No | The paper mentions using "synthetically generated linearly separable datasets" and "randomly sampled data" but does not specify any training, validation, or test splits for these datasets. |
| Hardware Specification | No | The code can be ran on Google Colab with a CPU runtime in under one hour. |
| Software Dependencies | No | The paper mentions that the code can be run on Google Colab with a CPU runtime and provides a GitHub link to Python code, but it does not specify any particular software dependencies with version numbers (e.g., Python version, specific library versions like PyTorch or TensorFlow). |
| Experiment Setup | No | Theorem 3.4 states a condition on the learning rate as "sufficiently small learning rate 0 < η < 2β 1σ 2 max (X)" but the paper does not specify concrete hyperparameter values (e.g., specific learning rate, batch size, number of epochs, optimizer settings) used for the experiments shown in Appendix I. |