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..
Efficient online learning with kernels for adversarial large scale problems
Authors: Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we perform in Section 4 several experiments based on real and simulated data to compare the performance (in regret and in time) of our methods with competitors. 4 Experiments We empirically test PKAWV against several state-of-the-art algorithms for online kernel regression. In particular, we test our algorithms in (1) an adversarial setting [see Appendix G], (2) on large scale datasets. The algorithms are evaluated on four datasets from UCI machine learning repository. In particular, casp (regression) and ijcnn1, cod-rna, SUSY (classification) [see Appendix G for casp and ijcnn1] ranging from 4 104 to 6 106 datapoints. For all datasets, we scaled x in [ 1, 1]d and y in [ 1, 1]. In Figs. 2 and 4 we show the average loss (square loss for regression and classification error for classification) and the computational costs of the considered algorithm. |
| Researcher Affiliation | Academia | INRIA Département d Informatique de l École Normale Supérieure PSL Research University, Paris, France |
| Pseudocode | No | The paper describes the PKAWV algorithm and its implementation details (e.g., 'Appendix H details how (4) can be efficiently implemented in these cases.'), but does not contain a clearly labeled pseudocode or algorithm block within the main text or appendices. |
| Open Source Code | Yes | The code necessary to reproduce the following experiments is available on Git Hub at https://github.com/Remjez/kernel-online-learning. |
| Open Datasets | Yes | The algorithms are evaluated on four datasets from UCI machine learning repository. In particular, casp (regression) and ijcnn1, cod-rna, SUSY (classification) [see Appendix G for casp and ijcnn1] ranging from 4 104 to 6 106 datapoints. |
| Dataset Splits | No | The paper describes an online learning setting where data is processed sequentially. It does not provide specific train/validation/test dataset split percentages, sample counts, or explicit splitting methodology for reproducing the data partitioning of the datasets used in experiments (casp, ijcnn1, cod-rna, SUSY). |
| Hardware Specification | Yes | All experiments have been done on a single desktop computer (Intel Core i7-6700) with a timeout of 5-min per algorithm. |
| Software Dependencies | No | The paper states 'The algorithms above have been implemented in python with numpy' but does not provide specific version numbers for Python, numpy, or any other libraries or solvers used. |
| Experiment Setup | Yes | For all algorithms and all experiments, we set σ = 1 [except for SUSY where σ = 4, to match accuracy results from RCR17] and λ = 1. When using KORS, we set µ = 1, β = 1 and ε = 0.5 as in [CLV17b]. The number of random-features in FOGD is fixed to 1000 and the learning rate η is 1/ n. |