Search Results for author:
Found 4 papers, 0 papers with code
Efficient reductions between some statistical models
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 model, and construct several computationally efficient such reductions between statistical experiments.
Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
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 iterative algorithm for the problem of recovering an unknown rank-1 matrix from rank-1 Gaussian measurements corrupted by noise.
Do algorithms and barriers for sparse principal component analysis extend to other structured settings?
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.
Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
Considering two prototypical choices for the nonlinearity, we study the convergence properties of a natural alternating update rule for this nonconvex optimization problem starting from a random initialization.
