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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
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Portfolio item number 2
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publications
Alternating Least Squares in Generalized Linear Models
Published in Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences), 2019
Description
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Excess-Risk Consistency of Group-hard Thresholding Estimator in Robust Estimation of Gaussian Mean
Published in Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences), 2020
This paper proves that using properly chosen threshold for group-hard thresholding estimator yields the optimal constant in front of parametric rate ($p/n$) in the risk bound for the expected error between the true mean value of Gaussian distribution and our estimator.
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All-in-one Robust Estimator for the Gaussian Mean
Published in Annals of Statistics (to appear), 2021
This paper presents a new robust estimator called Iteratively Re-weighted Mean (IRM) which enjoys 5 key properties desired for a robust estimator: computationally tractable, equivariant under similarity transformations, have a breakdown point around $0.28$, minimax optimal (up to logarithmic factor) and asymptotically efficient. IRM is obtained by an iterative reweighting approach assigning weights by solving a convex optimization problem (SDP). Dimension-free non-asymptotic risk bound for the expected error of the proposed estimator is proved. The results are extended for sub-Gaussian distributions, as well as for unknown contamination level or unknown covariance matrix. Joint work with A. S. Dalalyan.
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Optimal detection of the feature matching map in presence of noise and outliers
Published in Electronic Journal of Statistics (submitted), 2021
We consider the problem of finding the matching map between two sets of $d$ dimensional vectors from noisy observations, where the second set contains outliers. The main result shows that, in the high-dimensional setting, a detection region of unknown injection can be characterized by the sets of vectors for which the inlier-inlier distance is of order at least $d^{1/4}$ and the inlier-outlier distance is of order at least $d^{1/2}$. These rates are achieved using the estimated matching minimizing the sum of logarithms of distances between matched pairs of points. We also prove lower bounds establishing optimality of these rates. Joint work with T. Galstyan and A. S. Dalalyan.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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