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Found 26 papers, 10 papers with code
Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices
The second is rank allocation, where we choose the ranks of the blocks in each level, subject to the total rank having a given value, which preserves the total storage needed for the MLR matrix.
A Statistical View of Column Subset Selection
We consider the problem of selecting a small subset of representative variables from a large dataset.
RbX: Region-based explanations of prediction models
RbX is based on a greedy algorithm for building a convex polytope that approximates a region of feature space where model predictions are close to the prediction at some target point.
Weighted Low Rank Matrix Approximation and Acceleration
Low-rank matrix approximation is one of the central concepts in machine learning, with applications in dimension reduction, de-noising, multivariate statistical methodology, and many more.
Cross-validation: what does it estimate and how well does it do it?
Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood.
Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays
However, current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets with thousands of observational units or responses.
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach
Heterogeneous inference achieves divergent relevance, where relevance and diversity support each other as two collaborating objectives in one recommendation model, and where recommendation diversity is an inherent outcome of the relevance inference process.
Feature-weighted elastic net: using "features of features" for better prediction
In some supervised learning settings, the practitioner might have additional information on the features used for prediction.
Ridge Regularizaton: an Essential Concept in Data Science
Ridge or more formally $\ell_2$ regularization shows up in many areas of statistics and machine learning.
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Interpolators -- estimators that achieve zero training error -- have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type.
Modeling longitudinal data using matrix completion
From the statistical standpoint, such data is often analyzed in the context of a mixed-effect model where time is treated as both a fixed-effect (population progression curve) and a random-effect (individual variability).
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Using simulations, we show that using synth-validation to select a causal inference method for each study lowers the expected estimation error relative to consistently using any single method.
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
In exciting new work, Bertsimas et al. (2016) showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO) problem.
Methodology Computation
Some methods for heterogeneous treatment effect estimation in high-dimensions
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials.
Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball
We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression.
Saturating Splines and Feature Selection
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range.
Telugu OCR Framework using Deep Learning
In this paper, we address the task of Optical Character Recognition(OCR) for the Telugu script.
Confounder Adjustment in Multiple Hypothesis Testing
In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e. g. treatment variable, phenotype) and the outcome.
Methodology Statistics Theory Statistics Theory 62H25, 62J15
Generalized Additive Model Selection
We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension.
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.
Sparse Quadratic Discriminant Analysis and Community Bayes
We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models.
Compressive Feature Learning
This paper addresses the problem of unsupervised feature learning for text data.
A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression.
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
Our work builds on variance estimates for bagging proposed by Efron (1992, 2012) that are based on the jackknife and the infinitesimal jackknife (IJ).
Local case-control sampling: Efficient subsampling in imbalanced data sets
By contrast, our estimator is consistent for $\theta^*$ provided that the pilot estimate is.
