Sparse Gaussian conditional random fields: Algorithms, theory, and application to energy forecasting M Wytock, Z Kolter International conference on machine learning, 1265-1273, 2013 | 124 | 2013 |
Contextually supervised source separation with application to energy disaggregation M Wytock, J Kolter Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 98 | 2014 |
Machine learning for AC optimal power flow N Guha, Z Wang, M Wytock, A Majumdar arXiv preprint arXiv:1910.08842, 2019 | 81 | 2019 |
Large-scale probabilistic forecasting in energy systems using sparse gaussian conditional random fields M Wytock, JZ Kolter 52nd IEEE conference on decision and control, 1019-1024, 2013 | 49 | 2013 |
Dynamic energy management N Moehle, E Busseti, S Boyd, M Wytock Large Scale Optimization in Supply Chains and Smart Manufacturing: Theory …, 2019 | 26 | 2019 |
A fast algorithm for sparse controller design M Wytock, JZ Kolter arXiv preprint arXiv:1312.4892, 2013 | 23 | 2013 |
Dynamic energy management with scenario-based robust MPC M Wytock, N Moehle, S Boyd 2017 American Control Conference (ACC), 2042-2047, 2017 | 21 | 2017 |
Fast Newton methods for the group fused lasso. M Wytock, S Sra, JZ Kolter UAI, 888-897, 2014 | 19 | 2014 |
Epigraph projections for fast general convex programming PW Wang, M Wytock, Z Kolter International Conference on Machine Learning, 2868-2877, 2016 | 11 | 2016 |
Object recognition using template matching N Gupta, R Gupta, A Singh, M Wytock Available in: https://tmatch. googlecode. com/svnhistory/r38/trunk/report …, 2008 | 11 | 2008 |
Convex programming with fast proximal and linear operators M Wytock, PW Wang, JZ Kolter arXiv preprint arXiv:1511.04815, 2015 | 9 | 2015 |
Preventing cascading failures in microgrids with one-sided support vector machines M Wytock, S Salapaka, M Salapaka 53rd IEEE Conference on Decision and Control, 3252-3258, 2014 | 9 | 2014 |
A new architecture for optimization modeling frameworks M Wytock, S Diamond, F Heide, S Boyd 2016 6th Workshop on Python for High-Performance and Scientific Computing …, 2016 | 6 | 2016 |
Optimizing Optimization: Scalable Convex Programming with Proximal Operators. M Wytock Carnegie Mellon University, USA, 2016 | 3 | 2016 |
Sparse gaussian conditional random fields M Wytock, Z Kolter NIPS workshop on log-linear models, 2012 | 3 | 2012 |
Expedient and Parallelizable Sparse Coding Algorithm for Large Datasets Z Liang, R Deshmukh, JJ McNamara, M Wytock, JZ Kolter 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials …, 2016 | 2 | 2016 |
Course-specific search engines: semi-automated methods for identifying high quality topic-specific corpora N Guha, M Wytock Proceedings of the 22nd International Conference on World Wide Web, 1247-1252, 2013 | 2 | 2013 |
Probabilistic Segmentation via Total Variation Regularization M Wytock, JZ Kolter arXiv preprint arXiv:1511.04817, 2015 | 1 | 2015 |
Time Series Prediction and its Application to Energy Forecasting M Wytock, SJ Reddi | | 2012 |
Time-varying Linear Regression with Total Variation Regularization M Wytock | | |