Out-Of-Domain Unlabeled Data Improves Generalization

no code implementations • 29 Sep 2023 • Amir Hossein Saberi, Amir Najafi, Alireza Heidari, Mohammad Hosein Movasaghinia, Abolfazl Motahari, Babak H. Khalaj

From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in $\mathbb{R}^d$, where in addition to the $m$ independent and labeled samples from the true distribution, a set of $n$ (usually with $n\gg m$) out of domain and unlabeled samples are given as well.

Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes

no code implementations • 9 Sep 2022 • Amir Hossein Saberi, Amir Najafi, Seyed Abolfazl Motahari, Babak H. Khalaj

Also, we theoretically show that in order to achieve this bound, it is sufficient to have $n\ge\left(K^2/\varepsilon^2\right)e^{\Omega\left(K/\mathrm{SNR}^2\right)}$ samples, where $\mathrm{SNR}$ stands for the signal-to-noise ratio.

Density Estimation Vocal Bursts Intensity Prediction