1) Today at #ICML2024! Join us in Session 3 (11:30 CEST) to talk about how “Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes" puts forward technical proposals to leverage LLMs for data augmentation. 1/2

Are you at #Miccai2024? Join us this Sunday for our tutorial 'Clinical AI in the Real-World: From Data-Centric AI to Dynamic Learning'! I'm excited to present with Nabeel Seedat & Camila González as we explore methods to make healthcare AI ready for the real world 🏥MICCAI Society

3/25 Matchmaker: Self-Improving Large Language Model Programs for Schema Matching This paper introduces Matchmaker, a compositional language model program for schema matching that addresses the challenges of structural and semantic heterogeneity in data sources. It uses a

Join our #InspirationExchange session today about AI Model Evaluation! Nabeel Seedat & Paulius Rauba will present our upcoming #NeurIPS2024 work - learn how Context-Aware Testing ensures reliable ML: introducing a new paradigm for testing ML models. vanderschaar-lab.com/engagement-ses…

Introducing #CliMBDC! This LLM-powered copilot combines data-centric AI tools & advanced reasoning to solve complex data challenges in healthcare research & beyond. CliMB-DC can perform data management & build predictive models using natural language in a single conversation 1/2

Interested? Explore the architecture and capabilities of CliMB-DC on our website & watch our comprehensive engagement sessions where we demonstrate and discuss our copilot here: vanderschaar-lab.com/climb-dc/ You can access the CliMB-DC paper directly here: arxiv.org/pdf/2501.10321 2/2

Probably the 1ST data-centric LLM copilot, integrating various data-centric tools, supporting complex data processing and modeling of clinical problems🔥🔥 - Paper: Summarizes various data-related issues & corresponding tools, along with detailed case studies in the medical field

Scales++: New work at the intersection of efficient LLM Evals & Data Pruning using embeddings measuring the cognitive complexity of your eval data 🧠 📈 Get accurate evals using only 0.5% of your data with a reduction in upfront cost of over 18x! 👉 arxiv.org/abs/2510.26384

Do you need to use your full benchmark to evaluate your LLM? Check out our new work from Thomson Reuters Foundational Research on more efficient LLM evals