{"id":20431193,"url":"https://github.com/craft-ai/sci","last_synced_at":"2026-02-24T06:35:41.534Z","repository":{"id":145768318,"uuid":"97612640","full_name":"craft-ai/sci","owner":"craft-ai","description":"craft ai team scientific 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","machine-learning","scientific-publications"],"created_at":"2024-11-15T08:10:54.273Z","updated_at":"2026-02-24T06:35:41.484Z","avatar_url":"https://github.com/craft-ai.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [craft ai](http://craft.ai) scientific activities #\n\n## Publications ##\n- [(ICML 2024) Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteratio](https://icml.cc/virtual/2024/poster/33909)\n- [(CAP 2024) Confidentialité Pufferfish de Rényi : Mécanismes Additifs Généraux et Amplification de Confidentialité par Itération]()\n- [(preprint) The Impact of LoRA on the Emergence of Clusters in Transformers (2024)](https://arxiv.org/abs/2402.15415)\n- [(preprint) Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration (2024)](https://arxiv.org/abs/2312.13985)\n- [(ICML 2023 Tiny Paper)CausalStructCodec: Causally-aware observational and interventional data generator (2023)](https://openreview.net/pdf?id=cKLmwCTFiI)\n- [(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)\n- [(TPDP workshop ICML 2022) Practical considerations on using private sampling for synthetic data (2023)](https://arxiv.org/abs/2312.07139)\n- [(Trustworthy AI Workshop ECML/PKDD) XAI and geographic information: application to paleoenvironmental reconstructions (2022)](https://hal.science/hal-03773375v2)\n- [(ICPRAI 2022) Improving drift detection by monitoring shapley loss values](https://link.springer.com/chapter/10.1007/978-3-031-09282-4_38)\n- [(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)\n- [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)\n- [(EGC 2020)The three stages of Explainable AI: How explainability facilitates real world deployment of AI](./publications/2020-the-three-stages-of-xai)\n- [(AALTD Workshop ECML/PKDD 2019) A fully automated periodicity detection in time series](./publications/2019-a-fully-automated-periodicity-detection-in-time-series)\n- [(ICTAI 2018) Inducing Readable Oblique Decision Trees ](./publications/2018-inducing-readable-oblique-decision-trees)\n- [(ArXiv 2018) Information gain ratio correction: Improving prediction with more balanced decision tree splits](./publications/2018-Information-gain-ratio-correction)\n- [(PAAMS 2016) Forgetting Methods for White Box Learning ](./publications/2016-forgetting-methods-for-white-box-learning)\n- [(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)\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcraft-ai%2Fsci","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcraft-ai%2Fsci","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcraft-ai%2Fsci/lists"}