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https://github.com/craft-ai/sci

craft ai team scientific activities
https://github.com/craft-ai/sci

artificial-intelligence machine-learning scientific-publications

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craft ai team scientific activities

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# [craft ai](http://craft.ai) scientific activities #

## Publications ##
- [(ICML 2024) Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteratio](https://icml.cc/virtual/2024/poster/33909)
- [(CAP 2024) Confidentialité Pufferfish de Rényi : Mécanismes Additifs Généraux et Amplification de Confidentialité par Itération]()
- [(preprint) The Impact of LoRA on the Emergence of Clusters in Transformers (2024)](https://arxiv.org/abs/2402.15415)
- [(preprint) Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration (2024)](https://arxiv.org/abs/2312.13985)
- [(ICML 2023 Tiny Paper)CausalStructCodec: Causally-aware observational and interventional data generator (2023)](https://openreview.net/pdf?id=cKLmwCTFiI)
- [(CTHS 2023) De la fouille à la reconstitution des environnements et des comportements préhistoriques: l’outil de recherche Schopper, un système immersif en aide à la formulation (2023)](https://hal.science/hal-04197476/file/cths-17193.pdf)
- [(TPDP workshop ICML 2022) Practical considerations on using private sampling for synthetic data (2023)](https://arxiv.org/abs/2312.07139)
- [(Trustworthy AI Workshop ECML/PKDD) XAI and geographic information: application to paleoenvironmental reconstructions (2022)](https://hal.science/hal-03773375v2)
- [(ICPRAI 2022) Improving drift detection by monitoring shapley loss values](https://link.springer.com/chapter/10.1007/978-3-031-09282-4_38)
- [(UISPP 2021)](https://www.researchgate.net/publication/341201680_Evaluation_of_the_duration_of_the_anthropic_occupations_and_of_the_degree_of_mobility_of_human_Middle_Pleistocene_populations_at_the_Caune_de_l'Arago_Tautavel_France_First_results_of_an_innovative_mul)
- [Explainable and Transparent AI and MAS workshop EXTRAAMAS 2021) Towards an XAI-Assisted Third-Party Evaluation of AI Systems: Illustration on Decision Trees](https://link.springer.com/chapter/10.1007/978-3-030-82017-6_10)
- [(EGC 2020)The three stages of Explainable AI: How explainability facilitates real world deployment of AI](./publications/2020-the-three-stages-of-xai)
- [(AALTD Workshop ECML/PKDD 2019) A fully automated periodicity detection in time series](./publications/2019-a-fully-automated-periodicity-detection-in-time-series)
- [(ICTAI 2018) Inducing Readable Oblique Decision Trees ](./publications/2018-inducing-readable-oblique-decision-trees)
- [(ArXiv 2018) Information gain ratio correction: Improving prediction with more balanced decision tree splits](./publications/2018-Information-gain-ratio-correction)
- [(PAAMS 2016) Forgetting Methods for White Box Learning ](./publications/2016-forgetting-methods-for-white-box-learning)
- [(APIA 2017) Periodic split method: learning more readable decision trees for human activities (2017)](./publications/2017-periodic-split-method-learning-more-readable-decision-trees-for-human-activities)