✨ В следующую среду состоится семинар с уникальным гостем Cong Fang — ресерчер из Peking University. Встреча пройдёт на английском языке. Также обращаем внимание: при входе в конференцию необходимо авторизоваться под своим именем и фамилией — участников с ником «Гость» мы не сможем пропустить.

Seminar: Implicit Regularization of SGD in High-dimensional Linear Regression

🗓 When: October 29, 11:00 AM
🎙 Speaker: Cong Fang, Researcher at Peking University

What will the talk cover?
Stochastic Gradient Descent (SGD) is one of the most widely used algorithms in modern machine learning. In high-dimensional learning problems, the number of SGD iterations is often smaller than the number of model parameters, and the implicit regularization induced by the algorithm plays a key role in ensuring strong generalization performance.

In this seminar, we will:
🔵 Analyze the generalization behavior of SGD across different learning scenarios;
🔵 Compare learning efficiency under various scales — depending on data size and dimensionality;
🔵 Discuss the effects of covariate shift;
🔵 Present theoretical insights that inspire memory-efficient training algorithms for large language models (e.g., GPT-2)

Join link: https://us06web.zoom.us/j/89195790461?pwd=HmgS42lBtBe25a5Oa9baLMUacC9x4L.1

Come join us!