Nicolai Meinshausen

research

∙ 07/03/2023

Engression: Extrapolation for Nonlinear Regression?

Extrapolation is crucial in many statistical and machine learning applic...

0 Xinwei Shen, et al. ∙

research

∙ 02/11/2023

Confidence and Uncertainty Assessment for Distributional Random Forests

The Distributional Random Forest (DRF) is a recently introduced Random F...

0 Jeffrey Näf, et al. ∙

research

∙ 12/09/2022

Robust detection and attribution of climate change under interventions

Fingerprints are key tools in climate change detection and attribution (...

0 Eniko Székely, et al. ∙

research

∙ 04/21/2022

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

Estimating the effects of continuous-valued interventions from observati...

0 Andrew Jesson, et al. ∙

research

∙ 10/19/2021

fairadapt: Causal Reasoning for Fair Data Pre-processing

Machine learning algorithms are useful for various predictions tasks, bu...

0 Drago Plečko, et al. ∙

research

∙ 09/21/2021

PKLM: A flexible MCAR test using Classification

We develop a fully non-parametric, fast, easy-to-use, and powerful test ...

0 Loris Michel, et al. ∙

research

∙ 08/02/2021

ricu: R's Interface to Intensive Care Data

Providing computational infrastructure for handling diverse intensive ca...

0 Nicolas Bennett, et al. ∙

research

∙ 07/12/2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Despite decades of clinical research, sepsis remains a global public hea...

2 Michael Moor, et al. ∙

research

∙ 06/07/2021

Proper Scoring Rules for Missing Value Imputation

Given the prevalence of missing data in modern statistical research, a b...

0 Loris Michel, et al. ∙

research

∙ 05/29/2020

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

We propose an adaptation of the Random Forest algorithm to estimate the ...

0 Domagoj Ćevid, et al. ∙

research

∙ 05/12/2020

High Probability Lower Bounds for the Total Variation Distance

The statistics and machine learning communities have recently seen a gro...

0 Loris Michel, et al. ∙

research

∙ 11/15/2019

Fair Data Adaptation with Quantile Preservation

Fairness of classification and regression has received much attention re...

18 Drago Plečko, et al. ∙

research

∙ 10/08/2019

A direct approach to detection and attribution of climate change

We present here a novel statistical learning approach for detection and ...

0 Eniko Székely, et al. ∙

research

∙ 08/14/2019

Causal discovery in heavy-tailed models

Causal questions are omnipresent in many scientific problems. While much...

0 Nicola Gnecco, et al. ∙

research

∙ 11/13/2018

Spectral Deconfounding and Perturbed Sparse Linear Models

Standard high-dimensional regression methods assume that the underlying ...

0 Domagoj Ćevid, et al. ∙

research

∙ 11/02/2018

RSVP-graphs: Fast High-dimensional Covariance Matrix Estimation under Latent Confounding

In this work we consider the problem of estimating a high-dimensional p ...

0 Rajen D. Shah, et al. ∙

research

∙ 01/18/2018

Anchor regression: heterogeneous data meets causality

This is a preliminary draft of "Anchor regression: heterogeneous data me...

0 Dominik Rothenhäusler, et al. ∙

research

∙ 10/31/2017

Grouping-By-ID: Guarding Against Adversarial Domain Shifts

When training a deep network for image classification, one can broadly d...

0 Christina Heinze-Deml, et al. ∙

research

∙ 08/18/2017

Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence

The need to test whether two random vectors are independent has spawned ...

0 Luca Weihs, et al. ∙

research

∙ 03/01/2017

Preserving Differential Privacy Between Features in Distributed Estimation

Privacy is crucial in many applications of machine learning. Legal, ethi...

0 Christina Heinze-Deml, et al. ∙

research

∙ 11/21/2016

Scalable Adaptive Stochastic Optimization Using Random Projections

Adaptive stochastic gradient methods such as AdaGrad have gained popular...

0 Gabriel Krummenacher, et al. ∙

research

∙ 10/17/2016

The xyz algorithm for fast interaction search in high-dimensional data

When performing regression on a dataset with p variables, it is often of...

0 Gian-Andrea Thanei, et al. ∙

research

∙ 06/08/2015

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

We present DUAL-LOCO, a communication-efficient algorithm for distribute...

0 Christina Heinze, et al. ∙

research

∙ 06/08/2015

backShift: Learning causal cyclic graphs from unknown shift interventions

We propose a simple method to learn linear causal cyclic models in the p...

0 Dominik Rothenhäusler, et al. ∙

research

∙ 08/06/2013

On b-bit min-wise hashing for large-scale regression and classification with sparse data

Large-scale regression problems where both the number of variables, p, a...

0 Rajen D. Shah, et al. ∙

research

∙ 07/11/2013

Minimum Distance Estimation for Robust High-Dimensional Regression

We propose a minimum distance estimation method for robust regression in...

0 Aurélie C. Lozano, et al. ∙

research

∙ 03/25/2013

Random Intersection Trees

Finding interactions between variables in large and high-dimensional dat...

0 Rajen Dinesh Shah, et al. ∙

research

∙ 10/12/2009

Node harvest

When choosing a suitable technique for regression and classification wit...

0 Nicolai Meinshausen, et al. ∙

research

∙ 11/13/2008

P-values for high-dimensional regression

Assigning significance in high-dimensional regression is challenging. Mo...

0 Nicolai Meinshausen, et al. ∙

Nicolai Meinshausen

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