
Deep Learning for Functional Data Analysis with Adaptive Basis Layers
Despite their widespread success, the application of deep neural network...
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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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Deep Quantile Aggregation
Conditional quantile estimation is a key statistical learning challenge ...
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Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforce...
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Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles ...
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ResNeSt: SplitAttention Networks
While image classification models have recently continued to advance, mo...
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TraDE: Transformers for Density Estimation
We present TraDE, an attentionbased architecture for autoregressive de...
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Overinterpretation reveals image classification model pathologies
Image classifiers are typically scored on their test set accuracy, but h...
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AutoGluonTabular: Robust and Accurate AutoML for Structured Data
We introduce AutoGluonTabular, an opensource AutoML framework that req...
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Recognizing Variables from their Data via Deep Embeddings of Distributions
A key obstacle in automated analytics and metalearning is the inability...
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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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Latent Space Secrets of Denoising TextAutoencoders
While neural language models have recently demonstrated impressive perfo...
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Unsupervised Text Style Transfer via Iterative Matching and Translation
Text style transfer seeks to learn how to automatically rewrite sentence...
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What made you do this? Understanding blackbox decisions with sufficient input subsets
Local explanation frameworks aim to rationalize particular decisions mad...
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Lowrank Bandit Methods for Highdimensional Dynamic Pricing
We consider high dimensional dynamic multiproduct pricing with an evolv...
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Learning Optimal Interventions
Our goal is to identify beneficial interventions from observational data...
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Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
We introduce principal differences analysis (PDA) for analyzing differen...
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Jonas Mueller
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