no code implementations • 12 Oct 2023 • Xin Lyu, Avishay Tal, Hongxun Wu, Junzhao Yang
In this work, for any constant $q$, we prove tight memory-sample lower bounds for any parity learning algorithm that makes $q$ passes over the stream of samples.
no code implementations • 8 Aug 2017 • Sumegha Garg, Ran Raz, Avishay Tal
We show that any learning algorithm for the learning problem corresponding to $M$ requires either a memory of size at least $\Omega\left(k \cdot \ell \right)$, or at least $2^{\Omega(r)}$ samples.
