Online Robust Regression via SGD on the l1 loss
Authors: Scott Pesme, Nicolas Flammarion
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we provide experimental evidence of the efficiency of this simple and highly scalable algorithm. In this section we illustrate our theoretical results. We consider the experimental framework of [79] using synthetic datasets. |
| Researcher Affiliation | Academia | Scott Pesme EPFL Lausanne, Switzerland scott.pesme@epfl.ch Nicolas Flammarion EPFL Lausanne, Switzerland nicolas.flammarion@epfl.ch |
| Pseudocode | No | The SGD recursion is given by an equation: "θn = θn 1 + γnsgn (yn xn, θn 1 ) xn,", but this is not presented as a pseudocode block or a clearly labeled algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper states, "We consider the experimental framework of [79] using synthetic datasets." and describes how they are generated, but it does not provide access information (link, DOI, citation) to a publicly available dataset. |
| Dataset Splits | No | The paper describes the generation of synthetic data and the experimental setup (e.g., contamination models), but it does not explicitly provide details about train/validation/test splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions) used for the experiments. |
| Experiment Setup | Yes | We consider the experimental framework of [79] using synthetic datasets. The inputs xi are i.i.d. from N(0, H) where H is either the identity matrix (conditioning κ = 1) or a p.s.d matrix with eigenvalues (1/k)1 k d and random eigenvectors (κ = 1/d). The outputs yi are generated following yi = xi, θ + εi + bi where (εi)1 i n are i.i.d. from N(0, σ2) and the bi s are defined according to the following contamination model: for η > 0.5, a set of n/4 corruptions are set to 1000, another n/4 are set to 1000 and the rest (to reach proportion η > 0.5) are sampled from U([1, 10]). All results are averaged over five replications. We plot the convergence rate of averaged SGD on different loss functions: the ℓ1 loss, the ℓ2 loss and the Huber loss for which we consider various parameters. In the SGD setting this corresponds to a total of 5n iterations. |