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This paper considers the sparse Gaussian conditional random field, a discriminative extension of sparse inverse covariance estimation , where we use convex methods to learn a high-dimensional conditional distribution of outputs given inputs. The model has been proposed by multiple researchers within the past year, yet previous papers have been substantially… (More)

— Short-term forecasting is a ubiquitous practice in a wide range of energy systems, including forecasting demand, renewable generation, and electricity pricing. Although it is known that probabilistic forecasts (which give a distribution over possible future outcomes) can improve planning and control, many forecasting systems in practice are just used as "… (More)

We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We analyze the case of 2 loss… (More)

—We consider the task of designing sparse control laws for large-scale systems by directly minimizing an infinite horizon quadratic cost with an 1 penalty on the feedback controller gains. Our focus is on an improved algorithm that allows us to scale to large systems (i.e. those where sparsity is most useful) with convergence times that are several orders… (More)

We present a new algorithmic approach to the group fused lasso, a convex model that approximates a multi-dimensional signal via an approximately piecewise-constant signal. This model has found many applications in multiple change point detection, signal compression , and total variation denoising, though existing algorithms typically using first-order or… (More)

This paper develops an approach for efficiently solving general convex optimization problems specified as disciplined convex programs (DCP), a common general-purpose modeling framework. Specifically we develop an algorithm based upon fast epigraph projections, projections onto the epigraph of a convex function, an approach closely linked to proximal… (More)

— Microgrids formed by a network of power sources and power consumers yield significant advantages over the conventional power grid including proximity of power consumption to power generation, distributed generation, resiliency against wide area blackouts and ease of incorporation of renewable energy sources. On the other hand, unlike the conventional… (More)

- Neel Guha, Matt Wytock
- WWW
- 2013

Web search is an important research tool for many high school courses. However, generic search engines have a number of problems that arise out of not understanding the context of search (the high school course), leading to results that are off-topic or inappropriate as reference material. In this paper, we introduce the concept of a course-specific search… (More)

- Matt Wytock, J Zico Kolter, Ryan Tibshirani, Geoffrey Gordon, Stephen Boyd, Audra Iv
- 2016

Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming , general convex solvers have not yet reached sufficient maturity to fully de-couple the convex programming model from the numerical algorithms required for implementation. Especially as… (More)

- Matt Wytock, J Zico Kolter, Ryan Tibshirani, Geoffrey Gordon, Stephen Boyd
- 2016

Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming , general convex solvers have not yet reached sufficient maturity to fully de-couple the convex programming model from the numerical algorithms required for implementation. Especially as… (More)