b'Jonas Mueller'

research

∙ 09/02/2023

ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data

Despite powering sensitive systems like autonomous vehicles, object dete...

0 Ulyana Tkachenko, et al. ∙

research

∙ 08/30/2023

Quantifying Uncertainty in Answers from any Language Model via Intrinsic and Extrinsic Confidence Assessment

We introduce BSDetector, a method for detecting bad and speculative answ...

0 Jiuhai Chen, et al. ∙

research

∙ 07/11/2023

Estimating label quality and errors in semantic segmentation data via any model

The labor-intensive annotation process of semantic segmentation datasets...

0 Vedang Lad, et al. ∙

research

∙ 05/26/2023

Detecting Errors in Numerical Data via any Regression Model

Noise plagues many numerical datasets, where the recorded values in the ...

0 Hang Zhou, et al. ∙

research

∙ 05/25/2023

Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors

We present a straightforward statistical test to detect certain violatio...

0 Jesse Cummings, et al. ∙

research

∙ 01/27/2023

ActiveLab: Active Learning with Re-Labeling by Multiple Annotators

In real-world data labeling applications, annotators often provide imper...

0 Hui Wen Goh, et al. ∙

research

∙ 11/25/2022

Identifying Incorrect Annotations in Multi-Label Classification Data

In multi-label classification, each example in a dataset may be annotate...

0 Aditya Thyagarajan, et al. ∙

research

∙ 10/13/2022

Utilizing supervised models to infer consensus labels and their quality from data with multiple annotators

Real-world data for classification is often labeled by multiple annotato...

0 Hui Wen Goh, et al. ∙

research

∙ 10/08/2022

Detecting Label Errors in Token Classification Data

Mislabeled examples are a common issue in real-world data, particularly ...

0 Wei-Chen Wang, et al. ∙

research

∙ 10/04/2022

Data drift correction via time-varying importance weight estimator

Real-world deployment of machine learning models is challenging when dat...

0 Rasool Fakoor, et al. ∙

research

∙ 07/20/2022

DataPerf: Benchmarks for Data-Centric AI Development

Machine learning (ML) research has generally focused on models, while th...

17 Mark Mazumder, et al. ∙

research

∙ 07/07/2022

Back to the Basics: Revisiting Out-of-Distribution Detection Baselines

We study simple methods for out-of-distribution (OOD) image detection th...

0 Johnson Kuan, et al. ∙

research

∙ 06/16/2022

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features

Graph Neural Networks (GNNs) with numerical node features and graph stru...

0 Jiuhai Chen, et al. ∙

research

∙ 05/28/2022

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline

We study task-agnostic continual reinforcement learning (TACRL) in which...

11 Massimo Caccia, et al. ∙

research

∙ 11/04/2021

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

We consider the use of automated supervised learning systems for data ta...

0 Xingjian Shi, et al. ∙

research

∙ 10/26/2021

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features

For supervised learning with tabular data, decision tree ensembles produ...

4 Jiuhai Chen, et al. ∙

research

∙ 09/23/2021

Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing

We aim to identify how different components in the KD pipeline affect th...

0 Haoyu He, et al. ∙

research

∙ 06/19/2021

Deep Learning for Functional Data Analysis with Adaptive Basis Layers

Despite their widespread success, the application of deep neural network...

8 Junwen Yao, et al. ∙

research

∙ 03/26/2021

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

We algorithmically identify label errors in the test sets of 10 of the m...

2 Curtis G. Northcutt, et al. ∙

research

∙ 02/26/2021

Deep Quantile Aggregation

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

0 Taesup Kim, et al. ∙

research

∙ 02/18/2021

Continuous Doubly Constrained Batch Reinforcement Learning

Reliant on too many experiments to learn good actions, current Reinforce...

0 Rasool Fakoor, et al. ∙

research

∙ 06/25/2020

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation

Automated machine learning (AutoML) can produce complex model ensembles ...

0 Rasool Fakoor, et al. ∙

research

∙ 04/19/2020

ResNeSt: Split-Attention Networks

While image classification models have recently continued to advance, mo...

16 Hang Zhang, et al. ∙

research

∙ 04/06/2020

TraDE: Transformers for Density Estimation

We present TraDE, an attention-based architecture for auto-regressive de...

0 Rasool Fakoor, et al. ∙

research

∙ 03/19/2020

Overinterpretation reveals image classification model pathologies

Image classifiers are typically scored on their test set accuracy, but h...

20 Brandon Carter, et al. ∙

research

∙ 03/13/2020

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

We introduce AutoGluon-Tabular, an open-source AutoML framework that req...

4 Nick Erickson, et al. ∙

research

∙ 09/11/2019

Recognizing Variables from their Data via Deep Embeddings of Distributions

A key obstacle in automated analytics and meta-learning is the inability...

13 Jonas Mueller, et al. ∙

research

∙ 06/18/2019

Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles

The inaccuracy of neural network models on inputs that do not stem from ...

0 Siddhartha Jain, et al. ∙

research

∙ 05/29/2019

Latent Space Secrets of Denoising Text-Autoencoders

While neural language models have recently demonstrated impressive perfo...

0 Tianxiao Shen, et al. ∙

research

∙ 01/31/2019

Unsupervised Text Style Transfer via Iterative Matching and Translation

Text style transfer seeks to learn how to automatically rewrite sentence...

28 Zhijing Jin, et al. ∙

research

∙ 10/09/2018

What made you do this? Understanding black-box decisions with sufficient input subsets

Local explanation frameworks aim to rationalize particular decisions mad...

22 Brandon Carter, et al. ∙

research

∙ 01/30/2018

Low-rank Bandit Methods for High-dimensional Dynamic Pricing

We consider high dimensional dynamic multi-product pricing with an evolv...

0 Jonas Mueller, et al. ∙

research

∙ 06/16/2016

Learning Optimal Interventions

Our goal is to identify beneficial interventions from observational data...

0 Jonas Mueller, et al. ∙

research

∙ 10/30/2015

Principal Differences Analysis: Interpretable Characterization of Differences between Distributions

We introduce principal differences analysis (PDA) for analyzing differen...

0 Jonas Mueller, et al. ∙

Jonas Mueller

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