Madeleine Udell

Orcid: 0000-0002-3985-915X

According to our database1, Madeleine Udell authored at least 75 papers between 2014 and 2024.

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Bibliography

2024
gcimpute: A Package for Missing Data Imputation.
J. Stat. Softw., 2024

OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models.
CoRR, 2024

Challenges in Training PINNs: A Loss Landscape Perspective.
CoRR, 2024

2023
A strict complementarity approach to error bound and sensitivity of solution of conic programs.
Optim. Lett., September, 2023

Randomized Nyström Preconditioning.
SIAM J. Matrix Anal. Appl., June, 2023

Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis.
Trans. Mach. Learn. Res., 2023

Scalable Approximate Optimal Diagonal Preconditioning.
CoRR, 2023

OptiMUS: Optimization Modeling Using MIP Solvers and large language models.
CoRR, 2023

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates.
CoRR, 2023

Interpretable Survival Analysis for Heart Failure Risk Prediction.
Proceedings of the Machine Learning for Health, 2023

The Missing Indicator Method: From Low to High Dimensions.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Data-Efficient and Interpretable Tabular Anomaly Detection.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022
SketchySGD: Reliable Stochastic Optimization via Robust Curvature Estimates.
CoRR, 2022

ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles.
CoRR, 2022

Resource-Constrained Neural Architecture Search on Tabular Datasets.
CoRR, 2022

Towards Group Robustness in the presence of Partial Group Labels.
CoRR, 2022

Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

NysADMM: faster composite convex optimization via low-rank approximation.
Proceedings of the International Conference on Machine Learning, 2022

How Low Can We Go: Trading Memory for Error in Low-Precision Training.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Online Missing Value Imputation and Change Point Detection with the Gaussian Copula.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering.
IEEE Trans. Signal Process., 2021

Scalable Semidefinite Programming.
SIAM J. Math. Data Sci., 2021

Randomized Sketching Algorithms for Low-Memory Dynamic Optimization.
SIAM J. Optim., 2021

An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity.
SIAM J. Optim., 2021

On the Simplicity and Conditioning of Low Rank Semidefinite Programs.
SIAM J. Optim., 2021

Privileged Zero-Shot AutoML.
CoRR, 2021

Tensor Random Projection for Low Memory Dimension Reduction.
CoRR, 2021

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

ControlBurn: Feature Selection by Sparse Forests.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

CDF Normalization for Controlling the Distribution of Hidden Nodes.
Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

TenIPS: Inverse Propensity Sampling for Tensor Completion.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Low-Rank Tucker Approximation of a Tensor from Streaming Data.
SIAM J. Math. Data Sci., 2020

Dynamic Assortment Personalization in High Dimensions.
Oper. Res., 2020

Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization.
CoRR, 2020

Impact of Accuracy on Model Interpretations.
CoRR, 2020

Online Missing Value Imputation and Correlation Change Detection for Mixed-type Data via Gaussian Copula.
CoRR, 2020

An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction.
CoRR, 2020

kFW: A Frank-Wolfe style algorithm with stronger subproblem oracles.
CoRR, 2020

Efficient AutoML Pipeline Search with Matrix and Tensor Factorization.
CoRR, 2020

On the regularity and conditioning of low rank semidefinite programs.
CoRR, 2020

Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Approximate Cross-Validation with Low-Rank Data in High Dimensions.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Missing Value Imputation for Mixed Data via Gaussian Copula.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time.
Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Why Are Big Data Matrices Approximately Low Rank?
SIAM J. Math. Data Sci., 2019

Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation.
SIAM J. Sci. Comput., 2019

Optimal Design of Efficient Rooftop Photovoltaic Arrays.
INFORMS J. Appl. Anal., 2019

AutoML using Metadata Language Embeddings.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

OBOE: Collaborative Filtering for AutoML Model Selection.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Online High Rank Matrix Completion.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
OBOE: Collaborative Filtering for AutoML Initialization.
CoRR, 2018

Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Causal Inference with Noisy and Missing Covariates via Matrix Factorization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Practical Sketching Algorithms for Low-Rank Matrix Approximation.
SIAM J. Matrix Anal. Appl., 2017

Nice latent variable models have log-rank.
CoRR, 2017

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Graph-Regularized Generalized Low-Rank Models.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Generalized Low Rank Models.
Found. Trends Mach. Learn., 2016

Randomized single-view algorithms for low-rank matrix approximation.
CoRR, 2016

Bounding duality gap for separable problems with linear constraints.
Comput. Optim. Appl., 2016

Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices.
Proceedings of the 2016 ACM Conference on Economics and Computation, 2016

Discovering Patient Phenotypes Using Generalized Low Rank Models.
Proceedings of the Biocomputing 2016: Proceedings of the Pacific Symposium, 2016

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Learning Preferences from Assortment Choices in a Heterogeneous Population.
CoRR, 2015

Factorization for analog-to-digital matrix multiplication.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Revenue Maximization for Broadband Service Providers Using Revenue Capacity.
Proceedings of the 2015 IEEE Global Communications Conference, 2015

2014
Convex optimization in Julia.
Proceedings of the 1st First Workshop for High Performance Technical Computing in Dynamic Languages, 2014


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