Convex optimization in Julia. - Madeleine Udell; Karanveer Mohan; David Zeng; Jenny Hong; Steven Diamond; Stephen P. Boyd (HPTCDL@SC, 2014)

Revenue Maximization for Broadband Service Providers Using Revenue Capacity. - Haleema Mehmood; Madeleine Udell; John M. Cioffi (GLOBECOM, 2015)

Factorization for analog-to-digital matrix multiplication. - Edward H. Lee; Madeleine Udell; S. Simon Wong (ICASSP, 2015)

Learning Preferences from Assortment Choices in a Heterogeneous Population. - Nathan Kallus; Madeleine Udell (CoRR, 2015)

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. - Damek Davis; Brent Edmunds; Madeleine Udell (NIPS, 2016)

Discovering Patient Phenotypes Using Generalized Low Rank Models. - Alejandro Schuler; Vincent X. Liu; Joe Wan; Alison Callahan; Madeleine Udell; David E. Stark; Nigam H. Shah (PSB, 2016)

Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices. - Nathan Kallus; Madeleine Udell (EC, 2016)

Bounding duality gap for separable problems with linear constraints. - Madeleine Udell; Stephen P. Boyd (Comput. Optim. Appl., 2016)

Randomized single-view algorithms for low-rank matrix approximation. - Joel A. Tropp; Alp Yurtsever; Madeleine Udell; Volkan Cevher (CoRR, 2016)

Generalized Low Rank Models. - Madeleine Udell; Corinne Horn; Reza Zadeh; Stephen P. Boyd (Found. Trends Mach. Learn., 2016)

Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage. - Alp Yurtsever; Madeleine Udell; Joel A. Tropp; Volkan Cevher (AISTATS, 2017)

Graph-Regularized Generalized Low-Rank Models. - Mihir Paradkar; Madeleine Udell (CVPR Workshops, 2017)

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data. - Joel A. Tropp; Alp Yurtsever; Madeleine Udell; Volkan Cevher (NIPS, 2017)

Nice latent variable models have log-rank. - Madeleine Udell; Alex Townsend (CoRR, 2017)

Practical Sketching Algorithms for Low-Rank Matrix Approximation. - Joel A. Tropp; Alp Yurtsever; Madeleine Udell; Volkan Cevher (SIAM J. Matrix Anal. Appl., 2017)

Causal Inference with Noisy and Missing Covariates via Matrix Factorization. - Nathan Kallus; Xiaojie Mao; Madeleine Udell (NeurIPS, 2018)

Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives. - Song Zhou; Swati Gupta; Madeleine Udell (NeurIPS, 2018)

OBOE: Collaborative Filtering for AutoML Initialization. - Chengrun Yang; Yuji Akimoto; Dae Won Kim; Madeleine Udell (CoRR, 2018)

Online High Rank Matrix Completion. - Jicong Fan 001; Madeleine Udell (CVPR, 2019)

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. - Jiahao Chen 001; Nathan Kallus; Xiaojie Mao; Geoffry Svacha; Madeleine Udell (FAT, 2019)

OBOE: Collaborative Filtering for AutoML Model Selection. - Chengrun Yang; Yuji Akimoto; Dae Won Kim; Madeleine Udell (KDD, 2019)

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery. - Jicong Fan 001; Lijun Ding; Yudong Chen 001; Madeleine Udell (NeurIPS, 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

AutoML using Metadata Language Embeddings. - Iddo Drori; Lu Liu; Yi Nian; Sharath C. Koorathota; Jie S. Li; Antonio Khalil Moretti; Juliana Freire; Madeleine Udell (CoRR, 2019)

Optimal Design of Efficient Rooftop Photovoltaic Arrays. - Madeleine Udell; Oliver Toole (INFORMS J. Appl. Anal., 2019)

Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation. - Joel A. Tropp; Alp Yurtsever; Madeleine Udell; Volkan Cevher (SIAM J. Sci. Comput., 2019)

Why Are Big Data Matrices Approximately Low Rank? - Madeleine Udell; Alex Townsend (SIAM J. Math. Data Sci., 2019)

Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning. - Jicong Fan 001; Yuqian Zhang; Madeleine Udell (AAAI, 2020)

Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time. - Iddo Drori; Anant Kharkar; William R. Sickinger; Brandon Kates; Qiang Ma 004; Suwen Ge; Eden Dolev; Brenda Dietrich; David P. Williamson; Madeleine Udell (ICMLA, 2020)

AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space. - Chengrun Yang; Jicong Fan 001; Ziyang Wu; Madeleine Udell (KDD, 2020)

Missing Value Imputation for Mixed Data via Gaussian Copula. - Yuxuan Zhao; Madeleine Udell (KDD, 2020)

Approximate Cross-Validation with Low-Rank Data in High Dimensions. - William T. Stephenson; Madeleine Udell; Tamara Broderick (NeurIPS, 2020)

Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula. - Yuxuan Zhao; Madeleine Udell (NeurIPS, 2020)

On the regularity and conditioning of low rank semidefinite programs. - Lijun Ding; Madeleine Udell (CoRR, 2020)

Efficient AutoML Pipeline Search with Matrix and Tensor Factorization. - Chengrun Yang; Jicong Fan 001; Ziyang Wu; Madeleine Udell (CoRR, 2020)

kFW: A Frank-Wolfe style algorithm with stronger subproblem oracles. - Lijun Ding; Jicong Fan 001; Madeleine Udell (CoRR, 2020)

An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction. - Elizabeth A. Ricci; Madeleine Udell; Ross A. Knepper (CoRR, 2020)

Online Missing Value Imputation and Correlation Change Detection for Mixed-type Data via Gaussian Copula. - Yuxuan Zhao; Eric Landgrebe; Eliot Shekhtman; Madeleine Udell (CoRR, 2020)

Impact of Accuracy on Model Interpretations. - Brian Liu; Madeleine Udell (CoRR, 2020)

Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization. - Jicong Fan 001; Lijun Ding; Chengrun Yang; Madeleine Udell (CoRR, 2020)

Dynamic Assortment Personalization in High Dimensions. - Nathan Kallus; Madeleine Udell (Oper. Res., 2020)

Low-Rank Tucker Approximation of a Tensor from Streaming Data. - Yiming Sun; Yang Guo; Charlene Luo; Joel A. Tropp; Madeleine Udell (SIAM J. Math. Data Sci., 2020)

TenIPS: Inverse Propensity Sampling for Tensor Completion. - Chengrun Yang; Lijun Ding; Ziyang Wu; Madeleine Udell (AISTATS, 2021)

CDF Normalization for Controlling the Distribution of Hidden Nodes. - Mike Van Ness; Madeleine Udell (ICBINB@NeurIPS, 2021)

ControlBurn: Feature Selection by Sparse Forests. - Brian Liu; Miaolan Xie; Madeleine Udell (KDD, 2021)

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. - William T. Stephenson; Zachary Frangella; Madeleine Udell; Tamara Broderick (NeurIPS, 2021)

Tensor Random Projection for Low Memory Dimension Reduction. - Yiming Sun; Yang Guo; Joel A. Tropp; Madeleine Udell (CoRR, 2021)

Privileged Zero-Shot AutoML. - Nikhil Singh 003; Brandon Kates; Jeff Mentch; Anant Kharkar; Madeleine Udell; Iddo Drori (CoRR, 2021)

On the Simplicity and Conditioning of Low Rank Semidefinite Programs. - Lijun Ding; Madeleine Udell (SIAM J. Optim., 2021)

An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity. - Lijun Ding; Alp Yurtsever; Volkan Cevher; Joel A. Tropp; Madeleine Udell (SIAM J. Optim., 2021)

Randomized Sketching Algorithms for Low-Memory Dynamic Optimization. - Ramchandran Muthukumar; Drew P. Kouri; Madeleine Udell (SIAM J. Optim., 2021)

Scalable Semidefinite Programming. - Alp Yurtsever; Joel A. Tropp; Olivier Fercoq; Madeleine Udell; Volkan Cevher (SIAM J. Math. Data Sci., 2021)

Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering. - Jicong Fan 001; Chengrun Yang; Madeleine Udell (IEEE Trans. Signal Process., 2021)

Online Missing Value Imputation and Change Point Detection with the Gaussian Copula. - Yuxuan Zhao; Eric Landgrebe; Eliot Shekhtman; Madeleine Udell (AAAI, 2022)

How Low Can We Go: Trading Memory for Error in Low-Precision Training. - Chengrun Yang; Ziyang Wu; Jerry Chee; Christopher De Sa; Madeleine Udell (ICLR, 2022)

NysADMM: faster composite convex optimization via low-rank approximation. - Shipu Zhao; Zachary Frangella; Madeleine Udell (ICML, 2022)

TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets. - Chengrun Yang; Gabriel Bender; Hanxiao Liu; Pieter-Jan Kindermans; Madeleine Udell; Yifeng Lu; Quoc V. Le; Da Huang (NeurIPS, 2022)

Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data. - Yuxuan Zhao; Alex Townsend; Madeleine Udell (NeurIPS, 2022)

Towards Group Robustness in the presence of Partial Group Labels. - Vishnu Suresh Lokhande; Kihyuk Sohn; Jinsung Yoon; Madeleine Udell; Chen-Yu Lee; Tomas Pfister (CoRR, 2022)

Resource-Constrained Neural Architecture Search on Tabular Datasets. - Chengrun Yang; Gabriel Bender; Hanxiao Liu; Pieter-Jan Kindermans; Madeleine Udell; Yifeng Lu; Quoc V. Le; Da Huang (CoRR, 2022)

ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles. - Brian Liu; Miaolan Xie; Haoyue Yang; Madeleine Udell (CoRR, 2022)

SketchySGD: Reliable Stochastic Optimization via Robust Curvature Estimates. - Zachary Frangella; Pratik Rathore; Shipu Zhao; Madeleine Udell (CoRR, 2022)

Data-Efficient and Interpretable Tabular Anomaly Detection. - Chun-Hao Chang; Jinsung Yoon; Sercan Ö. Arik; Madeleine Udell; Tomas Pfister (KDD, 2023)

From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams. - Iddo Drori; Sarah J. Zhang; Reece Shuttleworth; Sarah Zhang; Keith Tyser; Zad Chin; Pedro Lantigua; Saisamrit Surbehera; Gregory Hunter; Derek Austin; Leonard Tang; Yann Hicke; et al. (KDD, 2023)

The Missing Indicator Method: From Low to High Dimensions. - Mike Van Ness; Tomas M. Bosschieter; Roberto Halpin-Gregorio; Madeleine Udell (KDD, 2023)

Interpretable Survival Analysis for Heart Failure Risk Prediction. - Mike Van Ness; Tomas M. Bosschieter; Natasha Din; Andrew Ambrosy; Alexander Sandhu; Madeleine Udell (ML4H@NeurIPS, 2023)

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates. - Zachary Frangella; Pratik Rathore; Shipu Zhao; Madeleine Udell (CoRR, 2023)

OptiMUS: Optimization Modeling Using MIP Solvers and large language models. - Ali AhmadiTeshnizi; Wenzhi Gao; Madeleine Udell (CoRR, 2023)

Scalable Approximate Optimal Diagonal Preconditioning. - Wenzhi Gao; Zhaonan Qu; Madeleine Udell; Yinyu Ye 001 (CoRR, 2023)

Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis. - Jicong Fan 001; Lijun Ding; Chengrun Yang; Zhao Zhang 001; Madeleine Udell (Trans. Mach. Learn. Res., 2023)

Randomized Nyström Preconditioning. - Zachary Frangella; Joel A. Tropp; Madeleine Udell (SIAM J. Matrix Anal. Appl., 2023)

A strict complementarity approach to error bound and sensitivity of solution of conic programs. - Lijun Ding; Madeleine Udell (Optim. Lett., 2023)

OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models. - Ali AhmadiTeshnizi; Wenzhi Gao; Madeleine Udell (ICML, 2024)

Challenges in Training PINNs: A Loss Landscape Perspective. - Pratik Rathore; Weimu Lei; Zachary Frangella; Lu Lu 015; Madeleine Udell (ICML, 2024)

DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models. - Mike Van Ness; Billy Block; Madeleine Udell (ML4H@NeurIPS, 2024)

Have ASkotch: Fast Methods for Large-scale, Memory-constrained Kernel Ridge Regression. - Pratik Rathore; Zachary Frangella; Madeleine Udell (CoRR, 2024)

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale. - Ali AhmadiTeshnizi; Wenzhi Gao; Herman Brunborg; Shayan Talaei; Madeleine Udell (CoRR, 2024)

AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews. - Keith Tyser; Ben Segev; Gaston Longhitano; Xin-Yu Zhang; Zachary Meeks; Jason Lee; Uday Garg; Nicholas Belsten; Avi Shporer; Madeleine Udell; Dov Te'eni; Iddo Drori (CoRR, 2024)

gcimpute: A Package for Missing Data Imputation. - Yuxuan Zhao; Madeleine Udell (J. Stat. Softw., 2024)

SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates. - Zachary Frangella; Pratik Rathore; Shipu Zhao; Madeleine Udell (SIAM J. Math. Data Sci., 2024)

Gradient Methods with Online Scaling. - Wenzhi Gao; Ya-Chi Chu; Yinyu Ye 001; Madeleine Udell (COLT, 2025)

LLMs for Cold-Start Cutting Plane Separator Configuration. - Connor Lawless; Yingxi Li; Anders Wikum; Madeleine Udell; Ellen Vitercik (CPAIOR (2), 2025)

Interpretable Prediction and Feature Selection for Survival Analysis. - Mike Van Ness; Madeleine Udell (KDD (1), 2025)

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning. - Jingruo Sun; Zachary Frangella; Madeleine Udell (CoRR, 2025)

Enhancing Physics-Informed Neural Networks Through Feature Engineering. - Shaghayegh Fazliani; Zachary Frangella; Madeleine Udell (CoRR, 2025)

Diverse Inference and Verification for Advanced Reasoning. - Iddo Drori; Gaston Longhitano; Mao Mao; Seunghwan Hyun; Yuke Zhang; Sungjun Park; Zachary Meeks; Xin-Yu Zhang; Ben Segev; Howard Yong; Nakul Verma; Avi Shporer; et al. (CoRR, 2025)

Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent. - Ya-Chi Chu; Wenzhi Gao; Yinyu Ye 001; Madeleine Udell (CoRR, 2025)

Understanding Fixed Predictions via Confined Regions. - Connor Lawless; Tsui-Wei Weng; Berk Ustun; Madeleine Udell (CoRR, 2025)

dnamite: A Python Package for Neural Additive Models. - Mike Van Ness; Madeleine Udell (CoRR, 2025)

Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project. - Pratik Rathore; Zachary Frangella; Sachin Garg; Shaghayegh Fazliani; Michal Derezinski; Madeleine Udell (CoRR, 2025)

Gradient Methods with Online Scaling Part I. Theoretical Foundations. - Wenzhi Gao; Ya-Chi Chu; Yinyu Ye 001; Madeleine Udell (CoRR, 2025)