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..
Early and Reliable Event Detection Using Proximity Space Representation
Authors: Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental studies provide compelling results on toy data, presenting the trade-off that occurs when aiming at accuracy, earliness and reliability. Results on real physiological and video datasets show that our proposed approach is as accurate and early as state-of-the-art algorithm, while ensuring reliability and being far more efficient to learn. |
| Researcher Affiliation | Academia | Maxime Sangnier EMAIL LTCI, CNRS, T el ecom Paris Tech, Universit e Paris-Saclay, 75013, Paris, France Jerome Gauthier EMAIL LADIS, CEA, LIST, 91191, Gif-sur-Yvette, France Alain Rakotomamonjy EMAIL Normandie Universit e, UR, LITIS EA 4108, Avenue de l universit e, 76801, Saint-Etienne-du-Rouvray, France |
| Pseudocode | Yes | Algorithm 1 Algorithm to learn a an early detector. 1: A random sample of indexes between 1 and m 2: repeat 3: Q yik(X(i) 1..T , pj) j A, 1 i n 4: β solve Problem (4) 5: j arg min 1 j m ((Qβ)j + µj) 6: j+ arg max 1 j m ((Qβ)j µj) 7: if µj λ (Qβ)j and (Qβ)j+ µj+ then 8: convergence 9: else if µj λ > (Qβ)j then 10: A A {j } 11: else 12: A A {j+} 13: end if 14: until convergence |
| Open Source Code | Yes | Matlab R code ran on a single core of an Intel R Xeon R E5-2630 CPU, operating at 2.4 GHz with GNU/Linux and 144 Gb of RAM. In addition, this code is available on the authors websites. |
| Open Datasets | Yes | For this experiment, we used the publicly available dataset 2a from BCI competition IV (Brunner et al., 2008).As a video-based experiment, we consider the extended Cohn-Kanade dataset (CK+) (Lucey et al., 2010). |
| Dataset Splits | Yes | C is obtained through a 5-fold crossvalidation (maximizing the AUROC) on the following grid [2 2, 20, . . . , 210]. |
| Hardware Specification | Yes | Matlab R code ran on a single core of an Intel R Xeon R E5-2630 CPU, operating at 2.4 GHz with GNU/Linux and 144 Gb of RAM. |
| Software Dependencies | No | The paper mentions 'Matlab R code' and 'GNU/Linux', and mentions 'lpsolve (Berkelaar et al., 2004)' as a tool, but does not provide specific version numbers for Matlab or lpsolve, or any other key software components used for the experiments. |
| Experiment Setup | Yes | The parameter γ that defines the landmarking space is set to 2 1 (default value for normalized data).C is obtained through a 5-fold crossvalidation (maximizing the AUROC) on the following grid [2 2, 20, . . . , 210]. |