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1 code implementation • 30 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.

1 code implementation • 24 Jul 2023 • Anav Sood, Trevor Hastie

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

no code implementations • 17 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.

no code implementations • 22 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.

2 code implementations • 1 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.

1 code implementation • 6 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.

no code implementations • 27 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.

1 code implementation • 2 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.

no code implementations • 30 May 2020 • Trevor Hastie

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

no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.

Applications

no code implementations • 19 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.

1 code implementation • 24 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).

no code implementations • 31 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.

1 code implementation • 27 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

1 code implementation • 1 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.

1 code implementation • 30 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.

no code implementations • 21 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.

no code implementations • 20 Sep 2015 • Rakesh Achanta, Trevor Hastie

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

no code implementations • 17 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

no code implementations • 11 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.

5 code implementations • 9 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.

no code implementations • 17 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.

no code implementations • NeurIPS 2013 • Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie

This paper addresses the problem of unsupervised feature learning for text data.

no code implementations • 26 Nov 2013 • Noah Simon, Jerome Friedman, Trevor Hastie

In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression.

no code implementations • 18 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).

no code implementations • 16 Jun 2013 • William Fithian, Trevor Hastie

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

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