4 accepted papers
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, fo…
Deep neural networks (DNNs) usually contain millions, maybe billions, of parameters/weights, making both storage and computation very expensive. This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers. Another well-known approac…
The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems. However, its performance is highly sensitive to a penalty parameter, making ADMM often unreliable and hard to automate for a non-expert user. We tackle this wea…
This paper studies the ordered weighted L1 (OWL) family of regularizers for sparse linear regression with strongly correlated covariates. We prove sufficient conditions for clustering correlated covariates, extending and qualitatively strengthening previous results for a particular member of the OW…