Madeleine Udell

All Publications

• Sparse Data Reconstruction, Missing Value and Multiple Imputation through Matrix Factorization SOCIOLOGICAL METHODOLOGY Sengupta, N., Udell, M., Srebro, N., Evans, J. 2023; 53 (1): 72-114

  View details for DOI 10.1177/00811750221125799

  View details for Web of Science ID 000924199900003

• A strict complementarity approach to error bound and sensitivity of solution of conic programs OPTIMIZATION LETTERS Ding, L., Udell, M. 2022

  View details for DOI 10.1007/s11590-022-01942-1

  View details for Web of Science ID 000878438700001

• NysADMM: faster composite convex optimization via low-rank approximation Zhao, S., Frangella, Z., Udell, M., Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

  View details for Web of Science ID 000900130208002

• Online Missing Value Imputation and Change Point Detection with the Gaussian Copula Zhao, Y., Landgrebe, E., Shekhtman, E., Udell, M., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2022: 9199-9207

  View details for Web of Science ID 000893639102025

• CONTROLBURN: Feature Selection by Sparse Forests Liu, B., Xie, M., Udell, M., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021: 1045-1054

  View details for DOI 10.1145/3447548.3467387

  View details for Web of Science ID 000749556801010

• Scalable Semidefinite Programming SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE Yurtsever, A., Tropp, J. A., Fercoq, O., Udell, M., Cevher, V. 2021; 3 (1): 171-200

  View details for DOI 10.1137/19M1305045

  View details for Web of Science ID 000646591200007

• Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering IEEE TRANSACTIONS ON SIGNAL PROCESSING Fan, J., Yang, C., Udell, M. 2021; 69: 1755-1770

  View details for DOI 10.1109/TSP.2021.3062988

  View details for Web of Science ID 000637182200002

• RANDOMIZED SKETCHING ALGORITHMS FOR LOW-MEMORY DYNAMIC OPTIMIZATION SIAM JOURNAL ON OPTIMIZATION Muthukumar, R., Kouri, D. P., Udell, M. 2021; 31 (2): 1242-1275

  View details for DOI 10.1137/19M1272561

  View details for Web of Science ID 000674142800007

• TenIPS: Inverse Propensity Sampling for Tensor Completion Yang, C., Ding, L., Wu, Z., Udell, M., Banerjee, A., Fukumizu, K. MICROTOME PUBLISHING. 2021

  View details for Web of Science ID 000659893803078

• ON THE SIMPLICITY AND CONDITIONING OF LOW RANK SEMIDEFINITE PROGRAMS SIAM JOURNAL ON OPTIMIZATION Ding, L., Udell, M. 2021; 31 (4): 2614-2637

  View details for DOI 10.1137/20M1346262

  View details for Web of Science ID 000738355700007

• An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity SIAM JOURNAL ON OPTIMIZATION Ding, L., Yurtsever, A., Cevher, V., Tropp, J. A., Udell, M. 2021; 31 (4): 2695-2725

  View details for DOI 10.1137/19M1244603

  View details for Web of Science ID 000738355700010

• Dynamic Assortment Personalization in High Dimensions OPERATIONS RESEARCH Kallus, N., Udell, M. 2020; 68 (4): 1020-1037

  View details for DOI 10.1287/opre.2019.1948

  View details for Web of Science ID 000556374400004

• Low-Rank Tucker Approximation of a Tensor from Streaming Data SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE Sun, Y., Guo, Y., Luo, C., Tropp, J., Udell, M. 2020; 2 (4): 1123-1150

  View details for DOI 10.1137/19M1257718

  View details for Web of Science ID 000646588300008

• AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space Yang, C., Fan, J., Wu, Z., Udell, M., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2020: 1446-1456

  View details for DOI 10.1145/3394486.3403197

  View details for Web of Science ID 000749552301045

• Missing Value Imputation for Mixed Data via Gaussian Copula Zhao, Y., Udell, M., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2020: 636-646

  View details for DOI 10.1145/3394486.3403106

  View details for Web of Science ID 000749552300064

• Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning Fan, J., Zhang, Y., Udell, M., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2020: 3842-3849

  View details for Web of Science ID 000667722803112

• Optimal Design of Efficient Rooftop Photovoltaic Arrays INFORMS JOURNAL ON APPLIED ANALYTICS Udell, M., Toole, O. 2019; 49 (4): 281-294

  View details for DOI 10.1287/inte.2019.1004

  View details for Web of Science ID 000483384100007

• Optimal Design of Efficient Rooftop Photovoltaic Arrays INFORMS JOURNAL ON APPLIED ANALYTICS Udell, M., Toole, O. 2019; 49 (4): 293-294

  View details for Web of Science ID 000483384100011

• Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery Fan, J., Ding, L., Chen, Y., Udell, M., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000534424305014

• Why Are Big Data Matrices Approximately Low Rank? SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE Udell, M., Townsend, A. 2019; 1 (1): 144-160

  View details for DOI 10.1137/18M1183480

  View details for Web of Science ID 000646577600007

• Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved Chen, J., Kallus, N., Mao, X., Svacha, G., Udell, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 339-348

  View details for DOI 10.1145/3287560.3287594

  View details for Web of Science ID 000473814700036

• STREAMING LOW-RANK MATRIX APPROXIMATION WITH AN APPLICATION TO SCIENTIFIC SIMULATION SIAM JOURNAL ON SCIENTIFIC COMPUTING Tropp, J. A., Yurtsever, A., Udell, M., Cevher, V. 2019; 41 (4): A2430-A2463

  View details for DOI 10.1137/18M1201068

  View details for Web of Science ID 000483924100015

• OBOE: Collaborative Filtering for AutoML Model Selection Yang, C., Akimoto, Y., Kim, D., Udell, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 1173-1183

  View details for DOI 10.1145/3292500.3330909

  View details for Web of Science ID 000485562501023

• Online high rank matrix completion Fan, J., Udell, M., IEEE Comp Soc IEEE. 2019: 8682-8690

  View details for DOI 10.1109/CVPR.2019.00889

  View details for Web of Science ID 000542649302031

• Causal Inference with Noisy and Missing Covariates via Matrix Factorization Kallus, N., Mao, X., Udell, M., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461852001046

• Frank-Wolfe Style Algorithms for Large Scale Optimization LARGE-SCALE AND DISTRIBUTED OPTIMIZATION Ding, L., Udell, M., Giselsson, P., Rantzer, A. 2018; 2227: 215-245

  View details for DOI 10.1007/978-3-319-97478-1_9

  View details for Web of Science ID 000458487300010

• Limited memory Kelley's Method Converges for Composite Convex and Submodular Objectives Zhou, S., Gupta, S., Udell, M., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461823304043

• Disciplined Multi-Convex Programming Shen, X., Diamond, S., Udell, M., Gu, Y., Boyd, S., IEEE IEEE. 2017: 895–900

  View details for Web of Science ID 000427082203091

• Graph-Regularized Generalized Low-Rank Models Paradkar, M., Udell, M., IEEE IEEE. 2017: 1921-1926

  View details for DOI 10.1109/CVPRW.2017.240

  View details for Web of Science ID 000426448300233

• PRACTICAL SKETCHING ALGORITHMS FOR LOW-RANK MATRIX APPROXIMATION SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS Tropp, J. A., Yurtsever, A., Udell, M., Cevher, V. 2017; 38 (4): 1454-1485

  View details for DOI 10.1137/17M1111590

  View details for Web of Science ID 000418665600017

• Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage Yurtsever, A., Udell, M., Tropp, J. A., Cevher, V., Singh, A., Zhu, J. MICROTOME PUBLISHING. 2017: 1188-1196

  View details for Web of Science ID 000509368500127

• Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data Tropp, J. A., Yurtsever, A., Udell, M., Cevher, V., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649401026

• Bounding duality gap for separable problems with linear constraints COMPUTATIONAL OPTIMIZATION AND APPLICATIONS Udell, M., Boyd, S. 2016; 64 (2): 355-378

  View details for DOI 10.1007/s10589-015-9819-4

  View details for Web of Science ID 000376872900002

• DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Schuler, A., Liu, V., Wan, J., Callahan, A., Udell, M., Stark, D. E., Shah, N. H. 2016; 21: 144-155

  Abstract

  The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.

  View details for PubMedID 26776181

• Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices Kallus, N., Udell, M., ACM ASSOC COMPUTING MACHINERY. 2016: 821-837

  View details for DOI 10.1145/2940716.2940794

  View details for Web of Science ID 000382226300081

• The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM Davis, D., Udell, M., Edmunds, B., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2016

  View details for Web of Science ID 000458973703064

• Introduction FOUNDATIONS AND TRENDS IN MACHINE LEARNING Udell, M., Horn, C., Zadeh, R., Boyd, S. 2016; 9 (1): 2-+

  View details for DOI 10.1561/2200000055

  View details for Web of Science ID 000383972700001

• FACTORIZATION FOR ANALOG-TO-DIGITAL MATRIX MULTIPLICATION Lee, E. H., Udell, M., Wong, S., IEEE IEEE. 2015: 1061–65

  View details for Web of Science ID 000427402901036

• Revenue Maximization for Broadband Service Providers Using Revenue Capacity Mehmood, H., Udell, M., Cioffi, J., IEEE IEEE. 2015

  View details for DOI 10.1109/GLOCOM.2015.7417861

  View details for Web of Science ID 000382389305113

• Incorporation of flexible objectives and time-linked simulation with flux balance analysis. Journal of theoretical biology Birch, E. W., Udell, M., Covert, M. W. 2014; 345: 12-21

  Abstract

  We present two modifications of the flux balance analysis (FBA) metabolic modeling framework which relax implicit assumptions of the biomass reaction. Our flexible flux balance analysis (flexFBA) objective removes the fixed proportion between reactants, and can therefore produce a subset of biomass reactants. Our time-linked flux balance analysis (tFBA) simulation removes the fixed proportion between reactants and byproducts, and can therefore describe transitions between metabolic steady states. Used together, flexFBA and tFBA model a time scale shorter than the regulatory and growth steady state encoded by the biomass reaction. This combined short-time FBA method is intended for integrated modeling applications to enable detailed and dynamic depictions of microbial physiology such as whole-cell modeling. For example, when modeling Escherichia coli, it avoids artifacts caused by low-copy-number enzymes in single-cell models with kinetic bounds. Even outside integrated modeling contexts, the detailed predictions of flexFBA and tFBA complement existing FBA techniques. We show detailed metabolite production of in silico knockouts used to identify when correct essentiality predictions are made for the wrong reason.

  View details for DOI 10.1016/j.jtbi.2013.12.009

  View details for PubMedID 24361328

  View details for PubMedCentralID PMC3933926

• Analyzing patterns of drug use in clinical notes for patient safety. AMIA Summits on Translational Science proceedings AMIA Summit on Translational Science LePendu, P., Liu, Y., Iyer, S., Udell, M. R., Shah, N. H. 2012; 2012: 63-70

  Abstract

  Doctors prescribe drugs for indications that are not FDA approved. Research indicates that 21% of prescriptions filled are for off-label indications. Of those, more than 73% lack supporting scientific evidence. Traditional drug safety alerts may not cover usages that are not FDA approved. Therefore, analyzing patterns of off-label drug usage in the clinical setting is an important step toward reducing the incidence of adverse events and for improving patient safety. We applied term extraction tools on the clinical notes of a million patients to compile a database of statistically significant patterns of drug use. We validated some of the usage patterns learned from the data against sources of known on-label and off-label use. Given our ability to quantify adverse event risks using the clinical notes, this will enable us to address patient safety because we can now rank-order off-label drug use and prioritize the search for their adverse event profiles.

  View details for PubMedID 22779054