Machine Learning Experts - Lewis Tunstall

Machine Learning Experts - Sasha Luccioni

Machine Learning Experts - Margaret Mitchell

Director of Machine Learning Insights [Part 4]

Director of Machine Learning Insights

Director of Machine Learning Insights [Part 2: SaaS Edition]

Director of Machine Learning Insights [Part 3: Finance Edition]

huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning

GPT-5 and the future of mathematical discovery

UCLA Professor Ernest Ryu and GPT-5 solved a key question in optimization theory, showcasing AI’s role in accelerating mathematical discovery.

The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models

The article discusses how predictive benchmarking—evaluating machine learning models by their performance and ranking—serves as a core method in machine learning research. It argues that benchmark scores only reflect performance on specific datasets and learning problems, and that drawing broader scientific conclusions requires explicit assumptions. By adapting concepts from psychological validity theory, the authors propose validity conditions to make these assumptions clear, and demonstrate their application in two case studies (ImageNet and the Fragile Families Challenge) to illustrate how benchmark results can inform inferences about research progress and limits of predictability.

Mixture of Experts Explained

Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation

arXiv:2603. 22320v2 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering.