activity
20132023
most citedResNeSt: Split-Attention Networks

456 citations · 984 across the 34 of their papers we have counts for

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7 papers · 1 filter

stat.ML2023★ 2 cited

Detecting Errors in a Numerical Response via any Regression Model

Hang Zhou, Jonas Mueller, Mayank Kumar +2

Noise plagues many numerical datasets, where the recorded values in the data may fail to match the true underlying values due to reasons including: erroneous sensors, data entry/pr…

stat.ML2021★ 12 cited

Deep Learning for Functional Data Analysis with Adaptive Basis Layers

Junwen Yao, Jonas Mueller, Jane-Ling Wang

Despite their widespread success, the application of deep neural networks to functional data remains scarce today. The infinite dimensionality of functional data means standard lea…

stat.ML2021★ 103 cited

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Curtis G. Northcutt, Anish Athalye, Jonas Mueller

We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these labe…

stat.ML2021★ 7 cited

Flexible Model Aggregation for Quantile Regression

Rasool Fakoor, Taesup Kim, Jonas Mueller +2

Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being over…

stat.ML2020★ 157 cited

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Nick Erickson, Jonas Mueller, Alexander Shirkov +4

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabula…

stat.ML2016★ 4 cited

Learning Optimal Interventions

Jonas Mueller, David N. Reshef, George Du +1

Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each…

stat.ML2015★ 22 cited

Principal Differences Analysis: Interpretable Characterization of Differences between Distributions

Jonas Mueller, Tommi Jaakkola

We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions. The method operates by finding the projection that maximizes the…