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09/02/2023
ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data
Despite powering sensitive systems like autonomous vehicles, object dete...
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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...
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07/11/2023
Estimating label quality and errors in semantic segmentation data via any model
The labor-intensive annotation process of semantic segmentation datasets...
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05/26/2023
Detecting Errors in Numerical Data via any Regression Model
Noise plagues many numerical datasets, where the recorded values in the ...
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05/25/2023
Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors
We present a straightforward statistical test to detect certain violatio...
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01/27/2023
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators
In real-world data labeling applications, annotators often provide imper...
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11/25/2022
Identifying Incorrect Annotations in Multi-Label Classification Data
In multi-label classification, each example in a dataset may be annotate...
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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...
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10/08/2022
Detecting Label Errors in Token Classification Data
Mislabeled examples are a common issue in real-world data, particularly ...
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10/04/2022
Data drift correction via time-varying importance weight estimator
Real-world deployment of machine learning models is challenging when dat...
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07/20/2022
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning (ML) research has generally focused on models, while th...
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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...
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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...
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05/28/2022
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline
We study task-agnostic continual reinforcement learning (TACRL) in which...
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11/04/2021
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
We consider the use of automated supervised learning systems for data ta...
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10/26/2021
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features
For supervised learning with tabular data, decision tree ensembles produ...
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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...
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06/19/2021
Deep Learning for Functional Data Analysis with Adaptive Basis Layers
Despite their widespread success, the application of deep neural network...
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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...
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02/26/2021
Deep Quantile Aggregation
Conditional quantile estimation is a key statistical learning challenge ...
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02/18/2021
Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforce...
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06/25/2020
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles ...
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04/19/2020
ResNeSt: Split-Attention Networks
While image classification models have recently continued to advance, mo...
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04/06/2020
TraDE: Transformers for Density Estimation
We present TraDE, an attention-based architecture for auto-regressive de...
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03/19/2020
Overinterpretation reveals image classification model pathologies
Image classifiers are typically scored on their test set accuracy, but h...
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03/13/2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
We introduce AutoGluon-Tabular, an open-source AutoML framework that req...
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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...
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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 ...
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05/29/2019
Latent Space Secrets of Denoising Text-Autoencoders
While neural language models have recently demonstrated impressive perfo...
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01/31/2019
Unsupervised Text Style Transfer via Iterative Matching and Translation
Text style transfer seeks to learn how to automatically rewrite sentence...
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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...
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01/30/2018
Low-rank Bandit Methods for High-dimensional Dynamic Pricing
We consider high dimensional dynamic multi-product pricing with an evolv...
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06/16/2016
Learning Optimal Interventions
Our goal is to identify beneficial interventions from observational data...
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10/30/2015
