The smallest singular value of inhomogenous random rectangular matrices

Let $A \in \mathbb{R}^{N \times n}$ ($N \geq n$) be a random matrix with with independent entries that have mean 0 variance 1 and bounded $2+\beta$ moment. We show that the smallest singular value $\sigma_n(A)$ satisfies \[ \Pr \left(\sigma_n(A) \leq \varepsilon(\sqrt{N+1} - \sqrt{n})\right) \leq (C\varepsilon)^{N-n+1} + e^{-cN}, \] for all $\varepsilon > 0$, where $c,C$ depend only on $\beta$ and the $2+\beta$ moment. This extends earlier results of Rudelson and Vershynin, who showed that such lower tail estimates held for rectangular matrices with i.i.d. mean 0 subgaussian entries. When the $2+\beta$ moment assumption is replaced with a uniform anti-concentration assumption, $\sup_z \Pr\left(|X-z| < a\right) < b$, we show that \[ \Pr\left(\sigma_n(A) \leq \varepsilon(\sqrt{N+1} - \sqrt{n})\right) \leq (C\varepsilon\log(1/\varepsilon))^{N-n+1} + e^{-cN}, \] where $c,C$ now depend only on $a$ and $b$. This extends more recent work of Livshyts, whose showed that such lower tail estimates held for rectrangular matrices with i.i.d. rows. To prove these results we employ a number of new technical ingredients, including a new deviation inequality for the regularized Hilbert-Schmidt norm and a recently proven small ball estimate for the distance between a random vector and a subspace spanned by an inhomogeneous rectangular matrix.