no code implementations • 2 Jul 2023 • Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh
We study lower bounds for the problem of approximating a one dimensional distribution given (noisy) measurements of its moments.
no code implementations • 3 Nov 2020 • Mihai Cucuringu, Apoorv Vikram Singh, Déborah Sulem, Hemant Tyagi
We study the problem of $k$-way clustering in signed graphs.
no code implementations • 28 Apr 2018 • Amit Deshpande, Anand Louis, Apoorv Vikram Singh
On the hardness side we show that for any $\alpha' > 1$, there exists an $\alpha \leq \alpha'$, $(\alpha >1)$, and an $\varepsilon_0 > 0$ such that minimizing the $k$-means objective over clusterings that satisfy $\alpha$-center proximity is NP-hard to approximate within a multiplicative $(1+\varepsilon_0)$ factor.