no code implementations • 12 Sep 2016 • Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
We consider the non-square matrix sensing problem, under restricted isometry property (RIP) assumptions.
no code implementations • 10 Jun 2016 • Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
We study such parameterization for optimization of generic convex objectives $f$, and focus on first-order, gradient descent algorithmic solutions.
no code implementations • 4 Jun 2016 • Dohyung Park, Anastasios Kyrillidis, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi
We study the projected gradient descent method on low-rank matrix problems with a strongly convex objective.
no code implementations • NeurIPS 2016 • Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis
For the partially observed case, we show the complexity of our algorithm is no more than $\mathcal{O}(r^4d \log d \log(1/\varepsilon))$.
1 code implementation • 16 Jul 2015 • Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit S. Dhillon
In this paper we consider the collaborative ranking setting: a pool of users each provides a small number of pairwise preferences between $d$ possible items; from these we need to predict preferences of the users for items they have not yet seen.
no code implementations • NeurIPS 2014 • Dohyung Park, Constantine Caramanis, Sujay Sanghavi
We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces.