Soda is thrilled to announce our projects were all funded by ANR AAPG 2025: ICPC – Intelligent Competitive Programming Companion – JCJC Jill-Jênn Vie StatQA – Foundation models for statistical question answering – JCJC Marine Le Morvan TaFoMo – Foundation models for tables – PRCE Gaël Varoquaux
The Soda team had a wonderful kick-off seminar on June 3, 2025! Link to presentations: Soda: vision and successes by Gaël Varoquaux Advancing tabular machine learning models and benchmarks by David Holzmüller TabICL: A Tabular Foundation Model for In-Context Learning on Large Data by Jingang Qu Statistical question answering: integrating…
Bénédicte Colnet brilliantly defended her PhD on generalization of causal effects across populations on June 29th 2023. The question she tackled is the following: to empirically evaluate a causal effect, randomized controlled trials (RCTs) give excellent control of confounding effects (internal validity), but they often are performed on a population…
The goal of our study is to evaluate the potential of the oneAPI-based software stack for ease of installation, interoperability, and performance on GPUs for sklearn popular estimators
The PhD defense of Alexis Cvetkov-Iliev on “Embedding models for relational data analytics” will take place on January 25 at 12:30 in Amphithéâtre Sophie Germain, Inria Saclay. It can also be attended remotely via the following link: https://inria.webex.com/meet/alexis.cvetkov-iliev. Short abstract of the defense:Data analysis, for instance with machine learning, typically…
Fast and More Efficient Machine Learning across Architectures [29 March, 2022] – Soda announces establishing an Intel oneAPI Center of Excellence for scikit-learn acceleration as part of a collaboration with Intel. In the information age, machine learning algorithms must efficiently manage large data sets. This requires scalable algorithms and efficient…
First paper with the soda affiliation: Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost (https://arxiv.org/abs/2203.07860) Making language models robust to unknown words (eg typos): a bit of contrastive learning can extend language models without retraining them! The idea of LOVE (Learning Out-of-Vocabulary Embeddings) is to map unknown…
The Soda team, with its research project, is born. We have been working on the project, and assembling the team for a while, so we are really excited to be going live!
