David Rousseau

I am excited to present Agent K as the first end-to-end agent (i.e., autonomous from Kaggle URL to submissions that win competitions) to achieve an equivalent of Kaggle grandmaster level. Our agent codes the whole data science pipeline from a natural language description of the competition and raw data! It does at least the following: 1. Cleans and pre-processing the data automatically; 2. Do feature engineering if needed automatically; 3. Write machine learning models that it thinks can solve the tasks automatically; 4. Trains the models and optimises their hyperparameters with HEBO automatically; 5. Write Kaggle submission files and decide to upload them to Kaggle to get the score automatically; It uses this score to improve its pipeline and submission automatically. Regarding results, we win six gold, three silver, and seven bronze medals. We also score in the top 38% against Kagglers. Since we win medals in all competition types, we make a fair comparison to human participants by awarding them extra medals if needed. Here, we also see that our Agent K is more likely to earn more medals than humans. The difference is particularly significant for bronze medals, where Agent K outperforms in 42% of match-ups and underperforms in only 23%. Similarly, for gold medals, the agent's winning rate of 14% is over twice its losing rate of 6%. How's that for LLMs that can't reason ;) Whoop whoop! #AI #machine_learning #MachineLearning #DataDriven #DataScientist #DataScientist arxiv.org/pdf/2411.03562

They made it! @Aishik_Ghosh_, Jay Sandesara, @dhpmrou and Rafael CLdS pushed through the first neural simulation-based inference analysis @ATLASexperiment. Huge congratulations to everyone involved!

.@brucedenby is the author of the very first paper on AI and Particle Physics back in 1988, when he was a post-doc in Orsay ( @IJCLab @cnrs_in2p3) working in the @CERN LEP DELPHI experiment. He was almost fired for this! #HEPML @dorigo @KyleCranmer @GregorKasieczka