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- Matt Wytock, J. Zico Kolter
- ICML
- 2013

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)

- Matt Wytock, J. Zico Kolter
- AAAI
- 2014

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)

- Matt Wytock, J. Zico Kolter
- CDC
- 2013

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)

Object Recognition is inherently a hard problem in computer vision. Current standard object recognition techniques require small training data sets of images and apply sophisticated algorithms. These methods tend to perform poorly because the small data set does not reflect the true distribution (selection bias). Recently, Torralba et al [1] have proposed… (More)

- Matt Wytock
- 2014

We consider modeling time series data with time-varying linear regression, a model that allows the weight matrix to vary at every time point but penalizes this variation with the (multivariate) total variation norm. This corresponds to simultaneously learning the parameters of multiple linear systems as well as the change points that describe when the… (More)

- Matt Wytock, J. Zico Kolter
- ArXiv
- 2013

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)

- Matt Wytock, Suvrit Sra, J. Zico Kolter
- UAI
- 2014

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)

- Brendan T. O’Connor, Tom Mitchell, +19 authors Yanchuan Sim
- 2014

What can text corpora tell us about society? How can automatic text analysis algorithms efficiently and reliably analyze the social processes revealed in language production? This work develops statistical text analyses of dynamic social and news media datasets to extract indicators of underlying social phenomena, and to reveal how social factors guide… (More)

- Po-Wei Wang, Matt Wytock, J. Zico Kolter
- ICML
- 2016

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)

- 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)