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..
Implicit Bias of Mirror Flow on Separable Data
Authors: Scott Pesme, Radu-Alexandru Dragomir, Nicolas Flammarion
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyse several examples of potentials and provide numerical experiments highlighting our results. |
| Researcher Affiliation | Academia | Scott Pesme EPFL Radu-Alexandru Dragomir Télécom Paris Nicolas Flammarion EPFL |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The code behind the experiments is straightforward and can easily be reproduced. |
| Open Datasets | No | As shown in Figure 1 (Middle), we generate 40 points with positive labels and 40 points with negative labels. Starting from β0 = 0, we run mirror descent with the exponential loss ℓ(z) = exp(−z) and with the three following potentials: (i) ϕGD = ‖·‖2, (ii) ϕMD1 = cosh-entropy, (iii) ϕMD2 = Hyperbolic entropy. |
| Dataset Splits | No | The paper describes generating a toy 2d dataset but does not provide specific training/test/validation split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. The NeurIPS checklist states: "It is clear that our experiments can easily be reproduced by any computer." |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. The NeurIPS checklist states: "The considered potentials and loss are given. The value of the step-size is not given as it does not have any relevance." |
| Experiment Setup | Yes | As shown in Figure 1 (Middle), we generate 40 points with positive labels and 40 points with negative labels. Starting from β0 = 0, we run mirror descent with the exponential loss ℓ(z) = exp(−z) and with the three following potentials: (i) ϕGD = ‖·‖2, (ii) ϕMD1 = cosh-entropy, (iii) ϕMD2 = Hyperbolic entropy. |