Search Results for author:
Found 8 papers, 1 papers with code
Stochastic algorithms under single spiked models
We study SGD and Adam for estimating a rank one signal planted in matrix or tensor noise.
Recognizing retinal ganglion cells in the dark
Here, we develop automated classifiers for functional identification of retinal ganglion cells, the output neurons of the retina, based solely on recorded voltage patterns on a large scale array.
Cone-Constrained Principal Component Analysis
We consider a simple model for noisy quadratic observation of an unknown vector $\bvz$.
A statistical model for tensor PCA
This is possibly related to a fundamental limitation of computationally tractable estimators for this problem.
Tight convex relaxations for sparse matrix factorization
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and known.
Link Prediction in Graphs with Autoregressive Features
In the paper, we consider the problem of link prediction in time-evolving graphs.
Estimation of Simultaneously Sparse and Low Rank Matrices
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time.
Link Discovery using Graph Feature Tracking
We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.
