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DTSTART;TZID=Europe/Paris:20251001T183000
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DTSTAMP:20260419T114625
CREATED:20250710T100429Z
LAST-MODIFIED:20250917T161351Z
UID:15544-1759343400-1759350600@datacraft.paris
SUMMARY:Machine Learning on tabular data using LLMs
DESCRIPTION:2509-Tinypred-LLMforFrugalAI \n \n\n \nInscription\n \n\nSouhaitez-vous participer en présentiel ou distanciel ? \n\nPrésentiel\nDistanciel\n\nValider inscription \n\n \nJe suis membre\, je me connecte\nJe ne suis pas membre mais souhaite quand même participer\n \n    \n        Connectez-vous pour vous inscrire à cet atelier. \n        \n            Email *Password / Mot de passe *J’ai oublié mon mot de passe Remember MeLogin / Se connecter\n            J’ai oublié mon mot de passe \n            Votre mot de passe vous a été envoyé par mail lors de votre\n                inscription au club. Vous pouvez le réinitialiser en cliquant sur le lien\n                ci-dessous :\n                Réinitialiser son mot de passe\n            \n        \n    \n    \n \n\n\nNom*\n\nPrénom*\n\nEmail*\n\nMétier*\n\nEntreprise*\n\nLinkedIn\n\nComment connaissez vous datacraft ?\n\n\nSouhaitez-vous participer en présentiel ou distanciel ? \nPrésentiel\nDistanciel\n\n\nValider inscription \n\nLoading…\n \nVous êtes bien inscrit !\nNous vous avons envoyé un save-the-date par email. \n\n \nErwan Bigan\, co-founder at TinyPred \nMachine Learning (ML) on tabular data mostly relies upon conventional algorithms like Logistic Regression\, Random Forest\, or gradient-boosted decision trees. \nAlthough there is no golden rule to determine how much data is actually required to train robust ML models\, it is generally believed that at least several hundreds or thousands of samples are needed for most practical applications\, thus restricting possible use cases. \nTinyPred has came up with a different approach : the use of Large Language Models for ML on tabular data. This method can require up to 5-10x fewer training data than traditional ML\, for the same predictive strength. The reason for better performance is that\, beyond inferring statistical patterns just like machine learning\, they benefit from their general knowledge expertise\, which allows them to interpret the data. \nIn this workshop\, we will :\n– Discuss new industrial use cases enabled by this LLM-based ML approach;\n– Present results obtained on published datasets\, which confirm the performance advantage for open source as well as proprietary LLMs;\n– Position this LLM-based ML approach in the broader context of foundational models for tabular ML: new pre-trained numerical deep learning models (e.g.\, TabICL\, TabPFN) have also been shown to deliver superior performance\, but for train sizes of several thousands of samples.
URL:https://datacraft.paris/event/machine-learning-on-tabular-data-using-llms/
LOCATION:Scaleway –\, 11bis Rue Roquépine\, Paris\, 75008\, France
CATEGORIES:Hybrid event,in English
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