Implicit Bias of Mirror Flow on Separable Data
Authors: Scott Pesme, Radu-Alexandru Dragomir, Nicolas Flammarion
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |