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Sep 19, 2020 at 23:35 comment added dohmatob @TerryTao Thanks for the crucial hint (namely, "small-ball probabilities"). Below, I've posted an answer putting every thing together.
Sep 19, 2020 at 23:22 answer added dohmatob timeline score: 1
Sep 19, 2020 at 15:04 comment added Terry Tao It's true (for any $C$, if $c$ is sufficiently small depending on $C$) for each individual column (i.e., for $n$=1) by direct calculation of the chi-squared distribution (or evaluating the measure that an $N$-dimensional gaussian gives to a small ball around the origin), and the epsilon net argument (see e.g., terrytao.wordpress.com/2010/03/05/… ) then extends this bound to tall matrices (again for any $C$, if $c,\lambda_0$ are small enough depending on $C$.)
Sep 19, 2020 at 15:00 comment added dohmatob Thanks for the input. Could you kindly provide a hight-level justification why this ought to be true for all $n/N < \lambda_0$, for some constant $\lambda_0 \in (0,1)$ ?
Sep 19, 2020 at 2:15 comment added Terry Tao I believe Rudelson and Vershynin are only claiming this bound in the regime of tall matrices (in which the ratio $n/N$ is assumed to be sufficiently small).
Sep 18, 2020 at 14:19 history edited dohmatob CC BY-SA 4.0
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Sep 18, 2020 at 13:52 history edited dohmatob CC BY-SA 4.0
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Sep 18, 2020 at 13:46 history asked dohmatob CC BY-SA 4.0