Engineered AAVs for non-invasive gene delivery to rodent and non-human primate nervous systems
- Award ID(s):
- 1707316
- NSF-PAR ID:
- 10350436
- Date Published:
- Journal Name:
- Neuron
- Volume:
- 110
- Issue:
- 14
- ISSN:
- 0896-6273
- Page Range / eLocation ID:
- 2242 to 2257.e6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
We present new algorithms for optimizing non-smooth, non-convex stochastic objectives based on a novel analysis technique. This improves the current best-known complexity for finding a (δ,ϵ)-stationary point from O(ϵ^(-4),δ^(-1)) stochastic gradient queries to O(ϵ^(-3),δ^(-1)), which we also show to be optimal. Our primary technique is a reduction from non-smooth non-convex optimization to online learning, after which our results follow from standard regret bounds in online learning. For deterministic and second-order smooth objectives, applying more advanced optimistic online learning techniques enables a new complexity of O(ϵ^(-1.5),δ^(-0.5)). Our techniques also recover all optimal or best-known results for finding ϵ stationary points of smooth or second-order smooth objectives in both stochastic and deterministic settings.more » « less