1 citations · 1 across the 7 of their papers we have counts for
7 papers
Efficient reductions from a Gaussian source with applications to statistical-computational tradeoffs
Mengqi Lou, Guy Bresler, Ashwin Pananjady
Given a single observation from a Gaussian distribution with unknown mean , we design computationally efficient procedures that can approximately generate an observation from a…
Accurate, provable and fast polychromatic tomographic reconstruction: A variational inequality approach
Mengqi Lou, Kabir Aladin Verchand, Sara Fridovich-Keil +1
We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ra…
Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
Mengqi Lou, Kabir Aladin Verchand, Ashwin Pananjady
Motivated by the desire to understand stochastic algorithms for nonconvex optimization that are robust to their hyperparameter choices, we analyze a mini-batched prox-linear iterat…
Computationally efficient reductions between some statistical models
Mengqi Lou, Guy Bresler, Ashwin Pananjady
We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source…
Do algorithms and barriers for sparse principal component analysis extend to other structured settings?
Guanyi Wang, Mengqi Lou, Ashwin Pananjady
We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general…
On seeded subgraph-to-subgraph matching: The ssSGM Algorithm and matchability information theory
Lingyao Meng, Mengqi Lou, Jianyu Lin +2
The subgraph-subgraph matching problem is, given a pair of graphs and a positive integer , to find vertices in the first graph, vertices in the second graph, and a bijec…
Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
Kabir Aladin Chandrasekher, Mengqi Lou, Ashwin Pananjady
We consider the problem of estimating the factors of a rank- matrix with i.i.d. Gaussian, rank- measurements that are nonlinearly transformed and corrupted by noise. Consider…