Author name cluster

Matt Wytock

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICML Conference 2016 Conference Paper

Epigraph projections for fast general convex programming

Po-Wei Wang
Matt Wytock
J. Zico Kolter

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 operator methods. We show that by using these operators, we can solve any disciplined convex program without transforming the problem to a standard cone form, as is done by current DCP libraries. We then develop a large library of efficient epigraph projection operators, mirroring and extending work on fast proximal algorithms, for many common convex functions. Finally, we evaluate the performance of the algorithm, and show it often achieves order of magnitude speedups over existing general-purpose optimization solvers.

Details

AAAI Conference 2014 Conference Paper

Contextually Supervised Source Separation with Application to Energy Disaggregation

Matt Wytock
J. Kolter

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. Contextually supervised source separation is a natural fit for domains with large amounts of data but no explicit supervision; our motivating application is energy disaggregation of hourly smart meter data (the separation of whole-home power signals into different energy uses). Here contextual supervision allows us to provide itemized energy usage for thousands homes, a task previously impossible due to the need for specialized data collection hardware. On smaller datasets which include labels, we demonstrate that contextual supervision improves significantly over a reasonable baseline and existing unsupervised methods for source separation. Finally, we analyze the case of `2 loss theoretically and show that recovery of the signal components depends only on cross-correlation between features for different signals, not on correlations between features for the same signal.

PDF Details

UAI Conference 2014 Conference Paper

Fast Newton methods for the group fused lasso

Matt Wytock
Suvrit Sra
J. Zico Kolter

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 alternating minimization schemes. In this paper we instead develop a specialized projected Newton method, combined with a primal active set approach, which we show to be substantially faster that existing methods. Furthermore, we present two applications that use this algorithm as a fast subroutine for a more complex outer loop: segmenting linear regression models for time series data, and color image denoising. We show that on these problems the proposed method performs very well, solving the problems faster than stateof-the-art methods and to higher accuracy.

Details

ICML Conference 2013 Conference Paper

Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting

Matt Wytock
J. Zico Kolter

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 limited in their analysis of the method and in the ability to solve large-scale problems. In this paper, we make three contributions: 1) we develop a second-order active-set method which is several orders of magnitude faster that previously proposed optimization approaches for this problem 2) we analyze the model from a theoretical standpoint, improving upon past bounds with convergence rates that depend logarithmically on the data dimension, and 3) we apply the method to large-scale energy forecasting problems, demonstrating state-of-the-art performance on two real-world tasks.

Details