Ryan J. Tibshirani

research

∙ 09/05/2023

Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test

Maximum mean discrepancy (MMD) refers to a general class of nonparametri...

0 Seunghoon Paik, et al. ∙

research

∙ 07/31/2023

Conformal PID Control for Time Series Prediction

We study the problem of uncertainty quantification for time series predi...

0 Anastasios N. Angelopoulos, et al. ∙

research

∙ 04/08/2023

De Finetti's Theorem and Related Results for Infinite Weighted Exchangeable Sequences

De Finetti's theorem, also called the de Finetti-Hewitt-Savage theorem, ...

0 Rina Foygel Barber, et al. ∙

research

∙ 12/30/2022

The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data

We consider the problem of estimating a multivariate function f_0 of bou...

0 Addison J. Hu, et al. ∙

research

∙ 12/04/2022

Coupled Bootstrap Test Error Estimation for Poisson Variables

Test error estimation is a fundamental problem in statistics and machine...

0 Natalia L. Oliveira, et al. ∙

research

∙ 11/07/2022

Asymptotics of the Sketched Pseudoinverse

We take a random matrix theory approach to random sketching and show an ...

0 Daniel LeJeune, et al. ∙

research

∙ 04/28/2022

Generalized permutation tests

Permutation tests are an immensely popular statistical tool, used for te...

0 Rina Foygel Barber, et al. ∙

research

∙ 02/27/2022

Conformal prediction beyond exchangeability

Conformal prediction is a popular, modern technique for providing valid ...

0 Rina Foygel Barber, et al. ∙

research

∙ 01/28/2022

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term bu...

0 Evan L. Ray, et al. ∙

research

∙ 12/29/2021

Multivariate Trend Filtering for Lattice Data

We study a multivariate version of trend filtering, called Kronecker tre...

13 Veeranjaneyulu Sadhanala, et al. ∙

research

∙ 12/13/2021

Real-Time Estimation of COVID-19 Infections via Deconvolution and Sensor Fusion

We propose, implement, and evaluate a method to estimate the daily numbe...

0 Maria Jahja, et al. ∙

research

∙ 12/12/2021

Recalibrating probabilistic forecasts of epidemics

Distributional forecasts are important for a wide variety of application...

0 Aaron Rumack, et al. ∙

research

∙ 11/17/2021

Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques

We study a new method for estimating the risk of an arbitrary estimator ...

0 Natalia L. Oliveira, et al. ∙

research

∙ 11/14/2021

Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs

In this paper we study the statistical properties of Principal Component...

0 Alden Green, et al. ∙

research

∙ 06/03/2021

Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs

In this paper we study the statistical properties of Laplacian smoothing...

0 Alden Green, et al. ∙

research

∙ 02/26/2021

Deep Quantile Aggregation

Conditional quantile estimation is a key statistical learning challenge ...

0 Taesup Kim, et al. ∙

research

∙ 03/17/2020

The Implicit Regularization of Stochastic Gradient Flow for Least Squares

We study the implicit regularization of mini-batch stochastic gradient d...

8 Alnur Ali, et al. ∙

research

∙ 03/09/2020

Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems

This paper serves as a postscript of sorts to Tibshirani (2014); Wang et...

0 Ryan J. Tibshirani, et al. ∙

research

∙ 02/28/2020

Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties

We propose a method for estimation in high-dimensional linear models wit...

0 Benjamin G. Stokell, et al. ∙

research

∙ 11/21/2019

Local Spectral Clustering of Density Upper Level Sets

We analyze the Personalized PageRank (PPR) algorithm, a local spectral m...

0 Alden Green, et al. ∙

research

∙ 05/27/2019

Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights

The Kalman filter (KF) is one of the most widely used tools for data ass...

0 David C. Farrow, et al. ∙

research

∙ 05/08/2019

Predictive inference with the jackknife+

This paper introduces the jackknife+, which is a novel method for constr...

0 Rina Foygel Barber, et al. ∙

research

∙ 04/12/2019

Conformal Prediction Under Covariate Shift

We extend conformal prediction methodology beyond the case of exchangeab...

0 Rina Foygel Barber, et al. ∙

research

∙ 03/24/2019

A Higher-Order Kolmogorov-Smirnov Test

We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, ...

16 Veeranjaneyulu Sadhanala, et al. ∙

research

∙ 03/19/2019

Surprises in High-Dimensional Ridgeless Least Squares Interpolation

Interpolators -- estimators that achieve zero training error -- have att...

0 Trevor Hastie, et al. ∙

research

∙ 03/12/2019

The limits of distribution-free conditional predictive inference

We consider the problem of distribution-free predictive inference, with ...

0 Rina Foygel Barber, et al. ∙

research

∙ 12/10/2018

Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data

Changepoint detection methods are used in many areas of science and engi...

0 Sangwon Hyun, et al. ∙

research

∙ 10/23/2018

A Continuous-Time View of Early Stopping for Least Squares Regression

We study the statistical properties of the iterates generated by gradien...

0 Alnur Ali, et al. ∙

research

∙ 05/20/2018

The Generalized Lasso Problem and Uniqueness

We study uniqueness in the generalized lasso problem, where the penalty ...

0 Alnur Ali, et al. ∙

research

∙ 07/27/2017

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

In exciting new work, Bertsimas et al. (2016) showed that the classical ...

0 Trevor Hastie, et al. ∙

research

∙ 02/16/2017

Additive Models with Trend Filtering

We consider additive models built with trend filtering, i.e., additive m...

0 Veeranjaneyulu Sadhanala, et al. ∙

research

∙ 04/14/2016

Distribution-Free Predictive Inference For Regression

We develop a general framework for distribution-free predictive inferenc...

0 Jing Lei, et al. ∙

research

∙ 01/29/2015

High-Dimensional Longitudinal Classification with the Multinomial Fused Lasso

We study regularized estimation in high-dimensional longitudinal classif...

0 Samrachana Adhikari, et al. ∙

research

∙ 12/04/2014

Nonparametric modal regression

Modal regression estimates the local modes of the distribution of Y give...

0 Yen-Chi Chen, et al. ∙

research

∙ 10/28/2014

Trend Filtering on Graphs

We introduce a family of adaptive estimators on graphs, based on penaliz...

0 Yu-Xiang Wang, et al. ∙

research

∙ 05/03/2014

The Falling Factorial Basis and Its Statistical Applications

We study a novel spline-like basis, which we name the "falling factorial...

0 Yu-Xiang Wang, et al. ∙

research

∙ 04/10/2013

Adaptive piecewise polynomial estimation via trend filtering

We study trend filtering, a recently proposed tool of Kim et al. [SIAM R...

0 Ryan J. Tibshirani, et al. ∙

research

∙ 11/09/2010

Strong rules for discarding predictors in lasso-type problems

We consider rules for discarding predictors in lasso regression and rela...

0 Robert Tibshirani, et al. ∙

Ryan J. Tibshirani

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