all AI news
Convergence Rates for Stochastic Approximation on a Boundary. (arXiv:2208.07243v2 [stat.ML] UPDATED)
Aug. 19, 2022, 1:11 a.m. | Kody Law, Neil Walton, Shangda Yang
stat.ML updates on arXiv.org arxiv.org
We analyze the behavior of projected stochastic gradient descent focusing on
the case where the optimum is on the boundary of the constraint set and the
gradient does not vanish at the optimum. Here iterates may in expectation make
progress against the objective at each step. When this and an appropriate
moment condition on noise holds, we prove that the convergence rate to the
optimum of the constrained stochastic gradient descent will be different and
typically be faster than the …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Lead Software Engineer - Artificial Intelligence, LLM
@ OpenText | Hyderabad, TG, IN
Lead Software Engineer- Python Data Engineer
@ JPMorgan Chase & Co. | GLASGOW, LANARKSHIRE, United Kingdom
Data Analyst (m/w/d)
@ Collaboration Betters The World | Berlin, Germany
Data Engineer, Quality Assurance
@ Informa Group Plc. | Boulder, CO, United States
Director, Data Science - Marketing
@ Dropbox | Remote - Canada