12,688 reputation
32470
bio website suvrit.de
location Berkeley, CA
age
visits member for 4 years
seen 2 hours ago

Researcher in Optimization, Machine Learning, etc.

Hobbyist in: Inequalities, Matrix Analysis, Noncommutative polynomials, and related algebras.

I'm here to learn; if you think there's any $\epsilon$ that I might be able to say to a problem of yours, please don't hesitate to contact me.


2d
reviewed Close Significance Testing for Periodic Component in additive of white noise
2d
reviewed Close How to prove a Proposition of Rouquier?
Aug
14
reviewed Close application of functional analysis in the field of Stochastic Approximation/Optimization
Aug
12
awarded  Yearling
Aug
10
reviewed Close How are two tailed p values (especially) and one tailed p values useful given the following?
Aug
9
comment Examples of research on how people perceive mathematical objects
I think there is a substantial conflation between math and physics going on here --- but I may be totally missing the point of this question....
Aug
7
comment Cauchy matrices with elementary symmetric polynomials
I think I proved this a few months ago! once I get a chance, I'll update this answer.
Jul
25
awarded  Notable Question
Jul
25
comment Examples of famous 'workhorse' theorems
I thought the question asked for "...technically challenging to prove..."
Jul
23
reviewed Leave Open Removing an article from arxiv
Jul
22
comment AI / Machine Learning related to high/modern/front mathematics
@RHahn: actually you'd be surprised; knowledge of measure theory is useful in ML research; see e.g., arxiv.org/abs/1202.6504, arxiv.org/abs/0907.5309, amongst many others.
Jul
22
comment AI / Machine Learning related to high/modern/front mathematics
This answer is a work in progress; I'll improve it with references to books and papers to make it more useful, as soon as I get a chance
Jul
22
answered AI / Machine Learning related to high/modern/front mathematics
Jul
22
comment A possible extension of a determinant inequality
@NathanielJohnston: nice find! Indeed that exercise of Bhatia proves $[\det(A_{ij})] \ge 0$, which seems to be an easier statement than the one shown above (more generally, the complete positivity of the immanant has been long known since at least the 60s)
Jul
16
comment Convergence rate of stochastic gradient decent with projections
Strong convexity implies strict convexity; Also, the convergence rate is unchanged (it is still O(1/t) ) --- that is, "stochastic projected subgradient" will have O(1/t) convergence rate for strongly convex problems (on a true stochastic optimization problem)
Jul
15
answered Is the prox-residual monotone?
Jul
10
awarded  Good Answer
Jul
10
reviewed Close Do's and don'ts of writing survey papers
Jul
8
awarded  Fanatic
Jul
7
reviewed Close How can I calculate $ \sum_{i=1}^{n} (n \bmod i) $