all AI news
Towards Sharp Stochastic Zeroth Order Hessian Estimators over Riemannian Manifolds. (arXiv:2201.10780v1 [stat.ML])
Web: http://arxiv.org/abs/2201.10780
Jan. 27, 2022, 2:10 a.m. | Tianyu Wang
cs.LG updates on arXiv.org arxiv.org
We study Hessian estimators for real-valued functions defined over an
$n$-dimensional complete Riemannian manifold. We introduce new stochastic
zeroth-order Hessian estimators using $O (1)$ function evaluations. We show
that, for a smooth real-valued function $f$ with Lipschitz Hessian (with
respect to the Rimannian metric), our estimator achieves a bias bound of order
$ O \left( L_2 \delta + \gamma \delta^2 \right) $, where $ L_2 $ is the
Lipschitz constant for the Hessian, $ \gamma $ depends on both the …
More from arxiv.org / cs.LG updates on arXiv.org
Latest AI/ML/Big Data Jobs
Senior Data Analyst
@ Fanatics Inc | Remote - New York
Data Engineer - Search
@ Cytora | United Kingdom - Remote
Product Manager, Technical - Data Infrastructure and Streaming
@ Nubank | Berlin
Postdoctoral Fellow: ML for autonomous materials discovery
@ Lawrence Berkeley National Lab | Berkeley, CA
Principal Data Scientist
@ Zuora | Remote
Data Engineer
@ Veeva Systems | Pennsylvania - Fort Washington