Papers
Papers below are listed by year of publication, or by year of submission before they are published.
For list of papers ordered by citation count, please see my
google scholar profile.
Streaming LowRank Matrix Approximation with an Application to Scientific Simulation
J. Tropp, A. Yurtsever, M. Udell, and V. Cevher
Submitted, 2019
[arxiv][bib]
An OptimalStorage Approach to Semidefinite Programming using Approximate Complementarity
L. Ding, A. Yurtsever, V. Cevher, J. Tropp, and M. Udell
Submitted, 2019
[arxiv][bib]
Big Data is Low Rank
M. Udell
SIAG/OPT Views and News, 2019
[url][slides][bib]
Online HighRank Matrix Completion
J. Fan and M. Udell
Computer Vision and Pattern Recognition, 2019
Oral Presentation
[bib]
Optimal Design of Efficient Rooftop Photovoltaic Arrays
M. Udell and O. Toole
Accepted at INFORMS Interfaces, 2019
Second place in Doing Good with Good OR Paper Competition, INFORMS 2017
[pdf][bib]
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
J. Chen, N. Kallus, X. Mao, G. Svacha, and M. Udell
FAT*: Conference on Fairness, Accountability, and Transparency, 2019
[arxiv][bib]
Why are Big Data Matrices Approximately Low Rank?
M. Udell and A. Townsend
SIAM Mathematics of Data Science (SIMODS), 2019
[arxiv][url][bib]
Tensor Random Projection for Low Memory Dimension Reduction
Y. Sun, Y. Guo, J. Tropp, and M. Udell
NeurIPS Workshop on Relational Representation Learning, 2018
[url][bib]
More Practical Sketching Algorithms for LowRank Matrix Approximation
J. Tropp, A. Yurtsever, M. Udell, and V. Cevher
California Institute of Technology ACM Technical Report 201801, 2018
[pdf][bib]
OBOE: Collaborative Filtering for AutoML Initialization
C. Yang, Y. Akimoto, D. Kim, and M. Udell
NeurIPS Workshop on Automated Machine Learning, 2018
[arxiv][bib]
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
S. Zhou, S. Gupta, and M. Udell
Advances in Neural Information Processing Systems, 2018
Spotlight presentation
[arxiv][pdf][slides][bib]
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
N. Kallus, X. Mao, and M. Udell
Advances in Neural Information Processing Systems, 2018
[arxiv][code][bib]
FrankWolfe Style algorithms for Large Scale Optimization
L. Ding and M. Udell
LargeScale and Distributed Optimization, 2018
[arxiv][pdf][bib]
Matrix Factorization for Missing Value Imputation and Sparse Data Reconstruction
N. Sengupta, M. Udell, N. Srebro, and J. Evans
Submitted, 2017
[bib]
FixedRank Approximation of a PositiveSemidefinite Matrix from Streaming Data
J. Tropp, A. Yurtsever, M. Udell, and V. Cevher
Advances in Neural Information Processing Systems, 2017
[arxiv][pdf][url][bib]
GraphRegularized Generalized Low Rank Models
M. Paradkar and M. Udell
CVPR Workshop on Tensor Methods in Computer Vision, 2017
[pdf][bib]
Sketchy Decisions: Convex LowRank Matrix Optimization with Optimal Storage
A. Yurtsever, M. Udell, J. Tropp, and V. Cevher
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
Oral Presentation
[arxiv][pdf][url][slides][bib][video]
Disciplined MultiConvex Programming
X. Shen, S. Diamond, M. Udell, Y. Gu, and S. Boyd
Chinese Control and Decision Conference (CCDC), 2017
Best Student Paper
[arxiv][bib]
Practical Sketching Algorithms for LowRank Matrix Approximation
J. Tropp, A. Yurtsever, M. Udell, and V. Cevher
SIAM Journal of Matrix Analysis and Applications (SIMAX), 2017
[arxiv][pdf][url][bib]
Dynamic Assortment Personalization in High Dimensions
N. Kallus and M. Udell
Under revision at Operations Research, 2016
[arxiv][bib]
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
D. Davis, B. Edmunds, and M. Udell
Advances in Neural Information Processing Systems, 2016
[arxiv][pdf][bib]
Discovering Patient Phenotypes Using Generalized Low Rank Models
A. Schuler, V. Liu, J. Wan, A. Callahan, M. Udell, D. Stark, and N. Shah
Pacific Symposium on Biocomputing (PSB), 2016
[pdf][url][bib]
Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices
N. Kallus and M. Udell
The 2016 ACM Conference on Economics and Computation, 2016
[pdf][url][bib]
Learning Preferences from Assortment Choices in a Heterogeneous Population
N. Kallus and M. Udell
ICML Workshop on Computational Frameworks for Personalization, 2016
[arxiv][bib]
Generalized Low Rank Models
M. Udell, C. Horn, R. Zadeh, and S. Boyd
Foundations and Trends in Machine Learning, 2016
[arxiv][pdf][url][slides][code][bib]
Bounding Duality Gap for Separable Problems with Linear Constraints
M. Udell and S. Boyd
Computational Optimization and Applications, 2016
[arxiv][pdf][url][bib]
Generalized Low Rank Models
M. Udell
Stanford University Thesis, 2015
[pdf][code][bib]
Revenue Maximization for Broadband Service Providers Using Revenue Capacity
H. Mehmood, M. Udell, and J. Cioffi
IEEE Global Communications Conference, 2015
[pdf][bib]
PCA on a Data Frame
M. Udell and S. Boyd
2015
[pdf][code][bib]
Factorization for AnalogtoDigital Matrix Multiplication
E. Lee, M. Udell, and S. Wong
ICASSP, 2015
[pdf][url][bib]
Beyond Principal Component Analysis (PCA)
M. Udell and S. Boyd
Biomedical Computation Review, 2014
[pdf][url][bib]
Convex Optimization in Julia
M. Udell, K. Mohan, D. Zeng, J. Hong, S. Diamond, and S. Boyd
SC14 Workshop on High Performance Technical Computing in Dynamic Languages, 2014
[arxiv][code][bib]
Generalized Low Rank Models
M. Udell, C. Horn, R. Zadeh, and S. Boyd
NeurIPS Workshop on Distributed Machine Learning and Matrix Computations, 2014
[pdf][code][bib]
Maximizing a Sum of Sigmoids
M. Udell and S. Boyd
2013
[pdf][code][bib]
Linear Bandits, Matrix Completion, and Recommendation Systems
M. Udell and R. Takapoui
NeurIPS Workshop on Large Scale Matrix Analysis and Inference, 2013
[pdf][bib]
Analyzing Patterns of Drug Use in Clinical Notes for Patient Safety
P. LePendu, Y. Liu, S. Iyer, M. Udell, and N. Shah
Proceedings of the AMIA Summits on Translational Science, 2012
[pdf][url][bib]
