no code implementations • NeurIPS 2017 • Christian Borgs, Jennifer Chayes, Christina E. Lee, Devavrat Shah
We show that the mean squared error (MSE) of our estimator converges to $0$ at the rate of $O(d^2 (pn)^{-2/5})$ as long as $\omega(d^5 n)$ random entries from a total of $n^2$ entries of $Y$ are observed (uniformly sampled), $\E[Y]$ has rank $d$, and the entries of $Y$ have bounded support.
no code implementations • NeurIPS 2016 • Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah
In contrast with classical regression, the features $x = (x_1(u), x_2(i))$ are not observed, making it challenging to apply standard regression methods to predict the unobserved ratings.
no code implementations • NeurIPS 2013 • Christina E. Lee, Asuman Ozdaglar, Devavrat Shah
In this paper, we provide a novel algorithm that answers whether a chosen state in a MC has stationary probability larger than some $\Delta \in (0, 1)$.