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.
Here are a few papers that give a good overview of my research.
ResourceConstrained Neural Architecture Search on Tabular Datasets
C. Yang, G. Bender, H. Liu, P. Kindermans, M. Udell, Y. Lu, Q. Le, and D. Huang
2022
[arxiv][bib]
gcimpute: A Package for Missing Data Imputation
Y. Zhao and M. Udell
2022
[arxiv][code][bib]
DataEfficient and Interpretable Tabular Anomaly Detection
C. Chang, J. Yoon, S. Arik, M. Udell, and T. Pfister
arXiv preprint arXiv:2203.02034, 2022
[bib]
NysADMM: faster composite convex optimization via lowrank approximation
S. Zhao, Z. Frangella, and M. Udell
Submitted, 2022
[arxiv][bib]
Towards Group Robustness in the presence of Partial Group Labels
V. S. Lokhande, K. Sohn, J. Yoon, M. Udell, C. Lee, and T. Pfister
CoRR, 2022
[url][bib]
How Low Can We Go: Trading Memory for Error in LowPrecision Training
C. Yang, Z. Wu, J. Chee, C. D. Sa, and M. Udell
International Conference on Learning Representations (ICLR), 2022
[arxiv][bib]
Matrix Factorization for Missing Value Imputation and Sparse Data Reconstruction
N. Sengupta, M. Udell, N. Srebro, and J. Evans
Accepted at Sociological Methodology, 2022
[bib]
CDF Normalization for Controlling the Distribution of Hidden Layer Activations
M. Van Ness and M. Udell
I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop, 2021
[bib]
Randomized Nystr\"om Preconditioning
Z. Frangella, J. A. Tropp, and M. Udell
Submitted, 2021
[arxiv][bib]
Can we globally optimize crossvalidation loss? Quasiconvexity in ridge regression
W. T. Stephenson, Z. Frangella, M. Udell, and T. Broderick
Advances in Neural Information Processing Systems (NeurIPS), 2021
[arxiv][bib]
Privileged ZeroShot AutoML
N. Singh, B. Kates, J. Mentch, A. Kharkar, M. Udell, and I. Drori
2021
[arxiv][bib]
An automatic system to detect equivalence between iterative algorithms
S. Zhao, L. Lessard, and M. Udell
Submitted, 2021
[arxiv][pdf][slides][bib]
ControlBurn: Feature Selection by Sparse Forests
B. Liu, M. Xie, and M. Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
[arxiv][slides][bib]
TenIPS: Inverse Propensity Sampling for Tensor Completion
C. Yang, L. Ding, Z. Wu, and M. Udell
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
[arxiv][bib]
Online Missing Value Imputation and Correlation Change Detection for Mixedtype Data via Gaussian Copula
Y. Zhao, E. Landgrebe, E. Shekhtman, and M. Udell
AAAI, 2021
[arxiv][bib]
Robust NonLinear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
J. Fan, C. Yang, and M. Udell
IEEE Trans. Signal Processing (TSP), 2021
[arxiv][pdf][url][bib]
On the simplicity and conditioning of low rank semidefinite programs
L. Ding and M. Udell
SIAM Journal on Optimization (SIOPT), 2021
[arxiv][url][bib]
Scalable Semidefinite Programming
A. Yurtsever, J. Tropp, O. Fercoq, M. Udell, and V. Cevher
SIAM Journal on Mathematics of Data Science (SIMODS), 2021
[arxiv][pdf][bib]
Randomized Sketching Algorithms for LowMemory Dynamic Optimization
R. Muthukumar, D. Kouri, and M. Udell
SIAM Journal on Optimization (SIOPT), 2021
[pdf][url][bib]
An OptimalStorage Approach to Semidefinite Programming using Approximate Complementarity
L. Ding, A. Yurtsever, V. Cevher, J. Tropp, and M. Udell
SIAM Journal on Optimization (SIOPT), 2021
Winner of 2017 INFORMS Optimization Society student paper prize
[arxiv][pdf][slides][bib]
Impact of Accuracy on Model Interpretations
B. Liu and M. Udell
2020
[arxiv][bib]
LowRank Tensor Recovery with EuclideanNormInduced Schattenp QuasiNorm Regularization
J. Fan, L. Ding, C. Yang, and M. Udell
Submitted, 2020
[arxiv][bib]
A Strict Complementarity Approach to Error Bound and Sensitivity of Solution of Conic Programs
L. Ding and M. Udell
Submitted, 2020
[arxiv][bib]
Galaxy TSP: A new billionnode benchmark for TSP
I. Drori, B. Kates, W. Sickinger, A. Kharkar, B. Dietrich, A. Shporer, and M. Udell
NeurIPS Workshop on Learning Meets Combinatorial Algorithms, 2020
[url][bib]
Approximate CrossValidation with LowRank Data in High Dimensions
W. Stephenson, M. Udell, and T. Broderick
Advances in Neural Information Processing Systems (NeurIPS), 2020
[arxiv][url][bib]
Online Mixed Missing Value Imputation Using Gaussian Copula
E. Landgrebe, Y. Zhao, and M. Udell
ICML Workshop on the Art of Learning with Missing Values (Artemiss), 2020
[bib]
TenIPS: Inverse Propensity Sampling for Tensor Completion (Workshop)
C. Yang, L. Ding, Z. Wu, and M. Udell
OPT2020: 12th Annual Workshop on Optimization for Machine Learning, 2020
[url][bib]
kFW: A FrankWolfe style algorithm with stronger subproblem oracles
L. Ding, J. Fan, and M. Udell
Submitted, 2020
[arxiv][bib]
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Y. Zhao and M. Udell
Advances in Neural Information Processing Systems (NeurIPS), 2020
[arxiv][pdf][bib]
Learning to Solve Combinatorial Optimization Problems on RealWorld Graphs in Linear Time
I. Drori, A. Kharkar, W. R. Sickinger, B. Kates, Q. Ma, S. Ge, E. Dolev, B. Dietrich, D. P. Williamson, and M. Udell
IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), 2020
[arxiv][bib]
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
C. Yang, J. Fan, Z. Wu, and M. Udell
2020
[arxiv][bib]
Zeroshot AutoML
I. Drori, L. Liu, Q. Ma, B. Kates, and M. Udell
Annual Machine Learning Symposium, 2020
[bib]
Realtime AutoML
I. Drori, L. Liu, Q. Ma, J. Deykin, B. Kates, and M. Udell
2020
[bib]
An InformationTheoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction
E. Ricci, M. Udell, and R. Knepper
2020
[arxiv][bib]
AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space
C. Yang, J. Fan, Z. Wu, and M. Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
[arxiv][url][bib]
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
J. Fan, Y. Zhang, and M. Udell
ThirtyFourth AAAI Conference on Artificial Intelligence, 2020
[arxiv][url][bib]
Missing Value Imputation for Mixed Data Through Gaussian Copula
Y. Zhao and M. Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
[arxiv][pdf][slides][bib]
LowRank Tucker Approximation of a Tensor From Streaming Data
Y. Sun, Y. Guo, C. Luo, J. Tropp, and M. Udell
SIAM Journal on Mathematics of Data Science (SIMODS), 2020
[arxiv][pdf][url][slides][bib]
SysML: The New Frontier of Machine Learning Systems
A. Ratner, D. Alistarh, G. Alonso, D. G. Andersen, P. Bailis, S. Bird, N. Carlini, B. Catanzaro, E. Chung, B. Dally, J. Dean, I. S. Dhillon, A. G. Dimakis, P. Dubey, C. Elkan, G. Fursin, G. R. Ganger, L. Getoor, P. B. Gibbons, G. A. Gibson, J. E. Gonzalez, J. Gottschlich, S. Han, K. M. Hazelwood, F. Huang, M. Jaggi, K. G. Jamieson, M. I. Jordan, G. Joshi, R. Khalaf, J. Knight, J. Konecn\'y, T. Kraska, A. Kumar, A. Kyrillidis, J. Li, S. Madden, H. B. McMahan, E. Meijer, I. Mitliagkas, R. Monga, D. G. Murray, D. S. Papailiopoulos, G. Pekhimenko, T. Rekatsinas, A. Rostamizadeh, C. R\'e, C. D. Sa, H. Sedghi, S. Sen, V. Smith, A. Smola, D. Song, E. R. Sparks, I. Stoica, V. Sze, M. Udell, J. Vanschoren, S. Venkataraman, R. Vinayak, M. Weimer, A. G. Wilson, E. P. Xing, M. Zaharia, C. Zhang, and A. Talwalkar
CoRR, 2019
[arxiv][url][bib]
Factor GroupSparse Regularization for Efficient LowRank Matrix Recovery
J. Fan, L. Ding, Y. Chen, and M. Udell
Advances in Neural Information Processing Systems (NeurIPS), 2019
[arxiv][pdf][bib]
AutoML using Metadata Language Embeddings
I. Drori, L. Liu, S. Koorathota, N. Yi, J. Li, A. Moretti, J. Freire, and M. Udell
NeurIPS Workshop on MetaLearning, 2019
[arxiv][pdf][bib]
OBOE: Collaborative Filtering for AutoML Model Selection
C. Yang, Y. Akimoto, D. Kim, and M. Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
Oral presentation
[arxiv][pdf][url][bib]
``Why should you trust my explanation?'' Understanding uncertainty in LIME explanations
Y. Zhang, K. Song, Y. Sun, S. Tan, and M. Udell
ICML Workshop AI for Social Good, 2019
[arxiv][bib]
Streaming LowRank Matrix Approximation with an Application to Scientific Simulation
J. Tropp, A. Yurtsever, M. Udell, and V. Cevher
SIAM Scientific Computing (SISC), 2019
[arxiv][pdf][url][bib]
Big Data is Low Rank
M. Udell
SIAG/OPT Views and News, 2019
[pdf][url][slides][bib][video]
Online HighRank Matrix Completion
J. Fan and M. Udell
Computer Vision and Pattern Recognition (CVPR), 2019
Oral Presentation
[pdf][bib]
Optimal Design of Efficient Rooftop Photovoltaic Arrays
M. Udell and O. Toole
INFORMS Journal on Applied Analytics (Interfaces), 2019
Second place in 2017 INFORMS Doing Good with Good OR Paper Competition
[pdf][url][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][pdf][slides][bib]
Why are Big Data Matrices Approximately Low Rank?
M. Udell and A. Townsend
SIAM Journal on Mathematics of Data Science (SIMODS), 2019
[arxiv][url][bib]
Dynamic Assortment Personalization in High Dimensions
N. Kallus and M. Udell
Operations Research, 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
[arxiv][pdf][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 (workshop version)
C. Yang, Y. Akimoto, D. Kim, and M. Udell
NeurIPS Workshop on Automated Machine Learning, 2018
[arxiv][pdf][url][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][video]
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]
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]
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
International Conference on Acoustics, Speech, and Signal Processing (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]
