Martin Arjovsky

Martin Arjovsky is a researcher in machine learning, focusing on how algorithms learn from data. He is known for developing techniques that help machine learning models understand and adapt across different environments or datasets, known as domain adaptation and invariance. His work aims to make models more robust, reliable, and capable of generalizing beyond their initial data, which is crucial for real-world applications like image recognition or natural language processing. Arjovsky's contributions help ensure that AI systems perform well even when faced with new or unseen data.