arXiv Machine Learning

Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise

arXiv:2607. 25492v2 Announce Type: replace Abstract: We study stochastic optimization with heavy-tailed gradient noise.

arXiv Machine Learning
Jun 26

Finding Stationary Points by Comparisons

arXiv:2606. 27082v1 Announce Type: new Abstract: We study the problem of finding stationary points of non-convex functions when access to the objective is provided only through a comparison oracle that, given two points, outputs which has the larger function value.

By Helin Wang, Chenyi Zhang, Xiwen Tao, Yexin Zhang, Tongyang Li
arXiv Machine Learning
Sep 25

On the SoS Certifiability of Log-Concave Distributions

arXiv:2609. 30105v1 Announce Type: new Abstract: For an arbitrary isotropic log-concave distribution $P$ on $\mathbb{R}^d$, we prove that the polynomial $(Cm)^m\|v\|_2^m - \mathbb{E}_{X\sim P}\langle X,v\rangle^m$ is a sum of squares for every even $m\ge2$, where $C>0$ is a universal constant.

By Aleksandr Storozhenko
arXiv Machine Learning
4d ago

Optimal Quantum-Classical Separations for Exact Learning

arXiv:2609.38073v1 Announce Type: cross Abstract: We study exact learning with membership queries for concept classes $\mathcal C\subseteq\{0,1\}^N$, focusing on the relationships among their determi...

By Srinivasan Arunachalam, Amin Shiraz Gilani, Nikhil S. Mande
Hugging Face Trending Papers
Sep 24

On the SoS Certifiability of Log-Concave Distributions

For an arbitrary isotropic log-concave distribution $P$ on $\mathbb{R}^d$, we prove that the polynomial $(Cm)^m\|v\|_2^m - \mathbb{E}_{X\sim P}\langle X,v\rangle^m$ is a sum of squares for every even $m\ge2$, where $C>0$ is a universal constant. This removes the dependence on the Poincaré constant in the theorem of Kothari and Steinhardt (arXiv:1711.