Ethan Caballero

Ethan Caballero is a PhD student and researcher at Mila - Quebec Artificial Intelligence Institute. Here are some key details about him:

Education and Research Focus

Ethan is currently pursuing his PhD at Mila, working primarily with David Krueger, Irina Rish, and Blake Richards.13 His research interests include:

• Out-of-distribution generalization
• Robustness and invariance in machine learning
• Neural scaling laws
• Forecasting neural network capabilities and alignment properties as data, model size, and compute increase2

Professional Experience

Prior to his PhD studies, Ethan has held several research and engineering positions:

• Student Researcher at Google DeepMind (July - December 2023)
• Research Assistant and Research Engineer/Developer at Mila (2019-2020)
• AI/Machine Learning Engineer at Graphcore (January - May 2018)
• Various consulting roles in machine learning and applied research for companies like doc.ai, Talla, and Loop AI Labs (2015-2016)1

Academic Contributions

Ethan has authored several influential papers in the field of machine learning:

• "Out-of-distribution generalization via risk extrapolation (rex)" (2021)
• "Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization" (2021)
• "Broken Neural Scaling Laws" (2022)4

Skills and Languages

Ethan is proficient in multiple programming languages and tools relevant to machine learning research, including:

• Python (native proficiency)
• C++ (professional working proficiency)
• LaTeX
• Various machine learning frameworks and tools1

Ethan's work focuses on advancing the field of artificial intelligence, particularly in areas related to generalization, scaling, and robustness of neural networks.