This paper introduces PROMISE (Preconditioned Stochastic
Optimization Me...
Missing data is common in applied data science, particularly for tabular...
We introduce SketchySGD, a stochastic quasi-Newton method that uses sket...
Many real-world datasets contain missing entries and mixed data types
in...
ControlBurn is a Python package to construct feature-sparse tree ensembl...
The best neural architecture for a given machine learning problem depend...
This article introduces the Python package gcimpute for missing data
imp...
Anomaly detection (AD) plays an important role in numerous applications....
Learning invariant representations is an important requirement when trai...
This paper introduces the Nyström PCG algorithm for solving a symmetric
...
Models like LASSO and ridge regression are extensively used in practice ...
Tree ensembles distribute feature importance evenly amongst groups of
co...
Low-precision arithmetic trains deep learning models using less energy, ...
Random projections reduce the dimension of a set of vectors while preser...
Tensors are widely used to represent multiway arrays of data. The recove...
The nuclear norm and Schatten-p quasi-norm of a matrix are popular rank
...
Model interpretations are often used in practice to extract real world
i...
Most data science algorithms require complete observations, yet many dat...
Robots can be used to collect environmental data in regions that are
dif...
Many recent advances in machine learning are driven by a challenging
tri...
This paper proposes a new variant of Frank-Wolfe (FW), called kFW. Stand...
Modern large scale datasets are often plagued with missing entries; inde...
Data scientists seeking a good supervised learning model on a new datase...
Combinatorial optimization algorithms for graph problems are usually des...
Low dimensional nonlinear structure abounds in datasets across computer
...
Low rank matrix recovery problems appear widely in statistics, combinato...
Recent advances in matrix completion enable data imputation in full-rank...
This paper develops new methods to recover the missing entries of a high...
This paper develops a new class of nonconvex regularizers for low-rank m...
Missing data imputation forms the first critical step of many data analy...
As a human choosing a supervised learning algorithm, it is natural to be...
This paper describes a new algorithm for computing a low-Tucker-rank
app...
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
This paper argues that randomized linear sketching is a natural tool for...
This paper develops a new storage-optimal algorithm that provably solves...
Assessing the fairness of a decision making system with respect to a
pro...
We introduce a few variants on Frank-Wolfe style algorithms suitable for...
Algorithm selection and hyperparameter tuning remain two of the most
cha...
Valid causal inference in observational studies often requires controlli...
Several important applications, such as streaming PCA and semidefinite
p...
Matrices of low rank are pervasive in big data, appearing in recommender...
This paper concerns a fundamental class of convex matrix optimization
pr...
We study the problem of dynamic assortment personalization with large,
h...
This paper develops a suite of algorithms for constructing low-rank
appr...
We consider the problem of learning the preferences of a heterogeneous
p...
This paper describes Convex, a convex optimization modeling framework in...
Principal components analysis (PCA) is a well-known technique for
approx...