Search Results for author: Trevor Hastie

Found 26 papers, 10 papers with code

Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices

1 code implementation30 Oct 2023 Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd

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

1 code implementation24 Jul 2023 Anav Sood, Trevor Hastie

We consider the problem of selecting a small subset of representative variables from a large dataset.

Dimensionality Reduction

RbX: Region-based explanations of prediction models

no code implementations17 Oct 2022 Ismael Lemhadri, Harrison H. Li, Trevor Hastie

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

no code implementations22 Sep 2021 Elena Tuzhilina, Trevor Hastie

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.

Dimensionality Reduction Low-Rank Matrix Completion +1

Cross-validation: what does it estimate and how well does it do it?

2 code implementations1 Apr 2021 Stephen Bates, Trevor Hastie, Robert Tibshirani

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

1 code implementation6 Oct 2020 Łukasz Kidziński, Francis K. C. Hui, David I. Warton, Trevor Hastie

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

no code implementations27 Sep 2020 Yifang Liu, Zhentao Xu, Qiyuan An, Yang Yi, Yanzhi Wang, Trevor Hastie

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.

Collaborative Filtering Recommendation Systems

Feature-weighted elastic net: using "features of features" for better prediction

1 code implementation2 Jun 2020 J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani

In some supervised learning settings, the practitioner might have additional information on the features used for prediction.

feature selection Multi-Task Learning

Ridge Regularizaton: an Essential Concept in Data Science

no code implementations30 May 2020 Trevor Hastie

Ridge or more formally $\ell_2$ regularization shows up in many areas of statistics and machine learning.

BIG-bench Machine Learning

Surprises in High-Dimensional Ridgeless Least Squares Interpolation

no code implementations19 Mar 2019 Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani

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.

Vocal Bursts Intensity Prediction

Modeling longitudinal data using matrix completion

1 code implementation24 Sep 2018 Łukasz Kidziński, Trevor Hastie

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).

Gaussian Processes Matrix Completion

Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset

no code implementations31 Oct 2017 Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, Nigam Shah

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.

Causal Inference

Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso

1 code implementation27 Jul 2017 Trevor Hastie, Robert Tibshirani, Ryan J. Tibshirani

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

1 code implementation1 Jul 2017 Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H. Shah, Trevor Hastie, Robert Tibshirani

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.

Vocal Bursts Intensity Prediction

Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball

1 code implementation30 Jun 2017 Scott Powers, Trevor Hastie, Robert Tibshirani

We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression.


Saturating Splines and Feature Selection

no code implementations21 Sep 2016 Nicholas Boyd, Trevor Hastie, Stephen Boyd, Benjamin Recht, Michael Jordan

We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range.

Additive models feature selection

Telugu OCR Framework using Deep Learning

no code implementations20 Sep 2015 Rakesh Achanta, Trevor Hastie

In this paper, we address the task of Optical Character Recognition(OCR) for the Telugu script.

General Classification Language Modelling +2

Confounder Adjustment in Multiple Hypothesis Testing

no code implementations17 Aug 2015 Jingshu Wang, Qingyuan Zhao, Trevor Hastie, Art B. Owen

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

no code implementations11 Jun 2015 Alexandra Chouldechova, Trevor Hastie

We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension.

Additive models Model Selection

Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

5 code implementations9 Oct 2014 Trevor Hastie, Rahul Mazumder, Jason Lee, Reza Zadeh

The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.

Matrix Completion

Sparse Quadratic Discriminant Analysis and Community Bayes

no code implementations17 Jul 2014 Ya Le, Trevor Hastie

We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models.

General Classification

A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression

no code implementations26 Nov 2013 Noah Simon, Jerome Friedman, Trevor Hastie

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

no code implementations18 Nov 2013 Stefan Wager, Trevor Hastie, Bradley Efron

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

no code implementations16 Jun 2013 William Fithian, Trevor Hastie

By contrast, our estimator is consistent for $\theta^*$ provided that the pilot estimate is.

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