We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

Calcium imaging with cellular resolution typically requires an animal to be tethered under a
microscope, which substantially restricts the range of behaviors that can be studied. To …

We introduce a family of adaptive estimators on graphs, based on penalizing the l 1 norm of
discrete graph differences. This generalizes the idea of trend filtering (Kim et al., 2009; …

We study TV regularization, a widely used technique for eliciting structured sparsity. In
particular, we propose efficient algorithms for computing prox-operators for lp-norm TV. The …

As populations boom and bust, the accumulation of genetic diversity is modulated, encoding
histories of living populations in present-day variation. Many methods exist to decode these …

Suppose we observe a random vector X from some distribution in a known family with
unknown parameters. We ask the following question: when is it possible to split X into two …

In this article, we present a fast and stable algorithm for solving a class of optimization
problems that arise in many statistical estimation procedures, such as sparse fused lasso …

We consider efficient implementations of the generalized lasso dual path algorithm given by
Tibshirani and Taylor in. We first describe a generic approach that covers any penalty matrix …

Upstream open reading frames (uORFs), located in transcript leaders (5′ UTRs), are potent
cis-acting regulators of translation and mRNA turnover. Recent genome-wide ribosome …

Appropriate selection of the penalty parameter is crucial to obtaining good performance from
the Alternating Direction Method of Multipliers (ADMM). While analytic results for optimal …