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09/05/2023
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
This paper introduces PROMISE (Preconditioned Stochastic Optimization Me...
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11/16/2022
The Missing Indicator Method: From Low to High Dimensions
Missing data is common in applied data science, particularly for tabular...
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11/16/2022
SketchySGD: Reliable Stochastic Optimization via Robust Curvature Estimates
We introduce SketchySGD, a stochastic quasi-Newton method that uses sket...
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10/13/2022
Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data
Many real-world datasets contain missing entries and mixed data types in...
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07/08/2022
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles
ControlBurn is a Python package to construct feature-sparse tree ensembl...
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04/15/2022
Resource-Constrained Neural Architecture Search on Tabular Datasets
The best neural architecture for a given machine learning problem depend...
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03/09/2022
gcimpute: A Package for Missing Data Imputation
This article introduces the Python package gcimpute for missing data imp...
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03/03/2022
Data-Efficient and Interpretable Tabular Anomaly Detection
Anomaly detection (AD) plays an important role in numerous applications....
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01/10/2022
Towards Group Robustness in the presence of Partial Group Labels
Learning invariant representations is an important requirement when trai...
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10/06/2021
Randomized Nyström Preconditioning
This paper introduces the Nyström PCG algorithm for solving a symmetric ...
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07/19/2021
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
Models like LASSO and ridge regression are extensively used in practice ...
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07/01/2021
ControlBurn: Feature Selection by Sparse Forests
Tree ensembles distribute feature importance evenly amongst groups of co...
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06/17/2021
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Low-precision arithmetic trains deep learning models using less energy, ...
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04/30/2021
Tensor Random Projection for Low Memory Dimension Reduction
Random projections reduce the dimension of a set of vectors while preser...
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01/01/2021
TenIPS: Inverse Propensity Sampling for Tensor Completion
Tensors are widely used to represent multiway arrays of data. The recove...
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12/07/2020
Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization
The nuclear norm and Schatten-p quasi-norm of a matrix are popular rank ...
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11/17/2020
Impact of Accuracy on Model Interpretations
Model interpretations are often used in practice to extract real world i...
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09/25/2020
Online Missing Value Imputation and Correlation Change Detection for Mixed-type Data via Gaussian Copula
Most data science algorithms require complete observations, yet many dat...
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09/02/2020
An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction
Robots can be used to collect environmental data in regions that are dif...
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08/24/2020
Approximate Cross-Validation with Low-Rank Data in High Dimensions
Many recent advances in machine learning are driven by a challenging tri...
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06/29/2020
kFW: A Frank-Wolfe style algorithm with stronger subproblem oracles
This paper proposes a new variant of Frank-Wolfe (FW), called kFW. Stand...
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06/18/2020
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Modern large scale datasets are often plagued with missing entries; inde...
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06/07/2020
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
Data scientists seeking a good supervised learning model on a new datase...
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06/06/2020
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time
Combinatorial optimization algorithms for graph problems are usually des...
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05/04/2020
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
Low dimensional nonlinear structure abounds in datasets across computer ...
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02/25/2020
On the regularity and conditioning of low rank semidefinite programs
Low rank matrix recovery problems appear widely in statistics, combinato...
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02/20/2020
Online high rank matrix completion
Recent advances in matrix completion enable data imputation in full-rank...
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12/15/2019
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
This paper develops new methods to recover the missing entries of a high...
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11/13/2019
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
This paper develops a new class of nonconvex regularizers for low-rank m...
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10/28/2019
Missing Value Imputation for Mixed Data Through Gaussian Copula
Missing data imputation forms the first critical step of many data analy...
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10/08/2019
AutoML using Metadata Language Embeddings
As a human choosing a supervised learning algorithm, it is natural to be...
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04/24/2019
Low-Rank Tucker Approximation of a Tensor From Streaming Data
This paper describes a new algorithm for computing a low-Tucker-rank app...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/22/2019
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
This paper argues that randomized linear sketching is a natural tool for...
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02/09/2019
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storage-optimal algorithm that provably solves...
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11/27/2018
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Assessing the fairness of a decision making system with respect to a pro...
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08/15/2018
Frank-Wolfe Style Algorithms for Large Scale Optimization
We introduce a few variants on Frank-Wolfe style algorithms suitable for...
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08/09/2018
OBOE: Collaborative Filtering for AutoML Initialization
Algorithm selection and hyperparameter tuning remain two of the most cha...
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06/03/2018
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Valid causal inference in observational studies often requires controlli...
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06/18/2017
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite p...
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05/21/2017
Nice latent variable models have log-rank
Matrices of low rank are pervasive in big data, appearing in recommender...
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02/22/2017
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
This paper concerns a fundamental class of convex matrix optimization pr...
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10/18/2016
Dynamic Assortment Personalization in High Dimensions
We study the problem of dynamic assortment personalization with large, h...
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08/31/2016
Randomized single-view algorithms for low-rank matrix approximation
This paper develops a suite of algorithms for constructing low-rank appr...
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09/17/2015
Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices
We consider the problem of learning the preferences of a heterogeneous p...
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10/17/2014
Convex Optimization in Julia
This paper describes Convex, a convex optimization modeling framework in...
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10/01/2014
