no code implementations • 25 Feb 2021 • Sitan Chen, Jerry Li, Ryan O'Donnell
We revisit the basic problem of quantum state certification: given copies of unknown mixed state $\rho\in\mathbb{C}^{d\times d}$ and the description of a mixed state $\sigma$, decide whether $\sigma = \rho$ or $\|\sigma - \rho\|_{\mathsf{tr}} \ge \epsilon$.
no code implementations • 3 Nov 2018 • Anindya De, Ryan O'Donnell, Rocco Servedio
We study the problem of learning an unknown mixture of $k$ rankings over $n$ elements, given access to noisy samples drawn from the unknown mixture.
no code implementations • 4 Mar 2017 • Anindya De, Ryan O'Donnell, Rocco Servedio
The population recovery problem is a basic problem in noisy unsupervised learning that has attracted significant research attention in recent years [WY12, DRWY12, MS13, BIMP13, LZ15, DST16].
no code implementations • 9 Dec 2016 • Anindya De, Ryan O'Donnell, Rocco Servedio
For any constant deletion rate $0 < \delta < 1$, we give a mean-based algorithm that uses $\exp(O(n^{1/3}))$ time and traces; we also prove that any mean-based algorithm must use at least $\exp(\Omega(n^{1/3}))$ traces.
