
Crossvalidation: what does it estimate and how well does it do it?
Crossvalidation is a widelyused technique to estimate prediction error...
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DINA: Estimating Heterogenous Treatment Effects in Exponential Family and Cox Model
We propose to use the difference in natural parameters (DINA) to quantif...
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Elastic Net Regularization Paths for All Generalized Linear Models
The lasso and elastic net are popular regularized regression models for ...
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Canonical Correlation Analysis in high dimensions with structured regularization
Canonical Correlation Analysis (CCA) is a technique for measuring the as...
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Generalized Matrix Factorization
Unmeasured or latent variables are often the cause of correlations betwe...
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Simultaneous Relevance and Diversity: A New Recommendation Inference Approach
Relevance and diversity are both important to the success of recommender...
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Backfitting for large scale crossed random effects regressions
Regression models with crossed random effect error models can be very ex...
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Featureweighted elastic net: using "features of features" for better prediction
In some supervised learning settings, the practitioner might have additi...
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Ridge Regularizaton: an Essential Concept in Data Science
Ridge or more formally ℓ_2 regularization shows up in many areas of stat...
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Assessment of Heterogeneous Treatment Effect Estimation Accuracy via Matching
We study the assessment of the accuracy of heterogeneous treatment effec...
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The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial ...
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Surprises in HighDimensional Ridgeless Least Squares Interpolation
Interpolators  estimators that achieve zero training error  have att...
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Longitudinal data analysis using matrix completion
In clinical practice and biomedical research, measurements are often col...
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SynthValidation: Selecting the Best Causal Inference Method for a Given Dataset
Many decisions in healthcare, business, and other policy domains are mad...
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Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
In exciting new work, Bertsimas et al. (2016) showed that the classical ...
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Some methods for heterogeneous treatment effect estimation in highdimensions
When devising a course of treatment for a patient, doctors often have li...
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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 penal...
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Saturating Splines and Feature Selection
We extend the adaptive regression spline model by incorporating saturati...
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Telugu OCR Framework using Deep Learning
In this paper, we address the task of Optical Character Recognition(OCR)...
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Generalized Additive Model Selection
We introduce GAMSEL (Generalized Additive Model Selection), a penalized ...
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Matrix Completion and LowRank SVD via Fast Alternating Least Squares
The matrixcompletion problem has attracted a lot of attention, largely ...
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Sparse Quadratic Discriminant Analysis and Community Bayes
We develop a class of rules spanning the range between quadratic discrim...
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A Blockwise Descent Algorithm for Grouppenalized Multiresponse and Multinomial Regression
In this paper we purpose a blockwise descent algorithm for grouppenaliz...
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Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
We study the variability of predictions made by bagged learners and rand...
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Local casecontrol sampling: Efficient subsampling in imbalanced data sets
For classification problems with significant class imbalance, subsamplin...
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The Graphical Lasso: New Insights and Alternatives
The graphical lasso FHT2007a is an algorithm for learning the structure ...
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Exact covariance thresholding into connected components for largescale Graphical Lasso
We consider the sparse inverse covariance regularization problem or grap...
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Strong rules for discarding predictors in lassotype problems
We consider rules for discarding predictors in lasso regression and rela...
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