In the recent episode of the Latent Space podcast, hosts Alessio and Swyx sat down with Tianqi Chen (TQ), an assistant professor at Carnegie Mellon University and a leading figure in the machine learning community. Tianqi wears many hats, including being associated with Catalyst Group and OctoML, and has a significant footprint in the open-source ecosystem, especially with projects like Apache TVM, XGBoost, and MXNet.
In a candid conversation, TQ shared that beyond his technical persona, he has a unique hobby of sketching design diagrams in real sketchbooks, chronicling his journey through various projects. These sketches serve as a blueprint for his software projects and provide a tangible record of his thought processes over the years.
Tianqi’s acclaimed project, XGBoost, came up for discussion, highlighting its origins and unexpected success. Originally designed as an alternative to the rising trend of deep learning models, XGBoost ended up establishing its own niche, particularly for tabular data where tree-based models excel. The discussion gravitated toward the balance and potential amalgamation of tree-based models and deep learning. TQ believes in the lasting relevance of tree-based models, especially considering their natural rules, scalability, and interoperability. The talk wrapped up with a glimpse into the future, hinting at the merging of transformer models and tree-based algorithms for enhanced data processing.