1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ML2025
Learning Overspecified Gaussian Mixtures Exponentially Fast with the EM Algorithm
Zhenisbek Assylbekov, Alan Legg, Artur Pak
We investigate the convergence properties of the EM algorithm when applied to overspecified Gaussian mixture models -- that is, when the number of components in the fitted model ex…
cs.LG2023★ 1 cited
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods
Rustem Takhanov, Maxat Tezekbayev, Artur Pak +3
The discrete logarithm problem is a fundamental challenge in number theory with significant implications for cryptographic protocols. In this paper, we investigate the limitations…
cs.LG2023
Gradient Descent Fails to Learn High-frequency Functions and Modular Arithmetic
Rustem Takhanov, Maxat Tezekbayev, Artur Pak +2
Classes of target functions containing a large number of approximately orthogonal elements are known to be hard to learn by the Statistical Query algorithms. Recently this classica…