Search Results for author: Jonas Mueller

Found 25 papers, 13 papers with code

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

1 code implementation4 Nov 2021 Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola

We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well.

AutoML

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features

no code implementations26 Oct 2021 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.

Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features

no code implementations ICLR 2022 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).

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

no code implementations EMNLP (sustainlp) 2021 Haoyu He, Xingjian Shi, Jonas Mueller, Zha Sheng, Mu Li, George Karypis

We aim to identify how different components in the KD pipeline affect the resulting performance and how much the optimal KD pipeline varies across different datasets/tasks, such as the data augmentation policy, the loss function, and the intermediate representation for transferring the knowledge between teacher and student.

Data Augmentation Hyperparameter Optimization

Deep Learning for Functional Data Analysis with Adaptive Basis Layers

2 code implementations19 Jun 2021 Junwen Yao, Jonas Mueller, Jane-Ling Wang

Despite their widespread success, the application of deep neural networks to functional data remains scarce today.

Dimensionality Reduction

Multimodal AutoML on Structured Tables with Text Fields

2 code implementations ICML Workshop AutoML 2021 Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alex Smola

We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well.

AutoML

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

2 code implementations26 Mar 2021 Curtis G. Northcutt, Anish Athalye, Jonas Mueller

Errors in test sets are numerous and widespread: we estimate an average of at least 3. 3% errors across the 10 datasets, where for example label errors comprise at least 6% of the ImageNet validation set.

Deep Quantile Aggregation

no code implementations26 Feb 2021 Taesup Kim, Rasool Fakoor, Jonas Mueller, Ryan J. Tibshirani, Alexander J. Smola

Conditional quantile estimation is a key statistical learning challenge motivated by the need to quantify uncertainty in predictions or to model a diverse population without being overly reductive.

Continuous Doubly Constrained Batch Reinforcement Learning

1 code implementation NeurIPS 2021 Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola

Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration.

reinforcement-learning

TraDE: A Simple Self-Attention-Based Density Estimator

no code implementations1 Jan 2021 Rasool Fakoor, Pratik Anil Chaudhari, Jonas Mueller, Alex Smola

We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.

Density Estimation Out-of-Distribution Detection

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

1 code implementation NeurIPS 2020 Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola

Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor estimators.

AutoML Data Augmentation

TraDE: Transformers for Density Estimation

no code implementations6 Apr 2020 Rasool Fakoor, Pratik Chaudhari, Jonas Mueller, Alexander J. Smola

We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.

Density Estimation Out-of-Distribution Detection

Overinterpretation reveals image classification model pathologies

2 code implementations NeurIPS 2021 Brandon Carter, Siddhartha Jain, Jonas Mueller, David Gifford

Here, we demonstrate that neural networks trained on CIFAR-10 and ImageNet suffer from overinterpretation, and we find models on CIFAR-10 make confident predictions even when 95% of input images are masked and humans cannot discern salient features in the remaining pixel-subsets.

Classification General Classification +1

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

7 code implementations13 Mar 2020 Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola

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 tabular dataset such as a CSV file.

Neural Architecture Search

Denoising Improves Latent Space Geometry in Text Autoencoders

no code implementations25 Sep 2019 Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi Jaakkola

Neural language models have recently shown impressive gains in unconditional text generation, but controllable generation and manipulation of text remain challenging.

Denoising Text Generation

Recognizing Variables from their Data via Deep Embeddings of Distributions

no code implementations11 Sep 2019 Jonas Mueller, Alex Smola

A key obstacle in automated analytics and meta-learning is the inability to recognize when different datasets contain measurements of the same variable.

Meta-Learning

Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles

no code implementations18 Jun 2019 Siddhartha Jain, Ge Liu, Jonas Mueller, David Gifford

The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized.

Educating Text Autoencoders: Latent Representation Guidance via Denoising

3 code implementations ICML 2020 Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi Jaakkola

We prove that this simple modification guides the latent space geometry of the resulting model by encouraging the encoder to map similar texts to similar latent representations.

Denoising Style Transfer +1

IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation

3 code implementations IJCNLP 2019 Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, Enrico Santus

Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.

Style Transfer Text Attribute Transfer +2

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

1 code implementation9 Oct 2018 Brandon Carter, Jonas Mueller, Siddhartha Jain, David Gifford

Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model.

Decision Making

Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing

1 code implementation NeurIPS 2019 Jonas Mueller, Vasilis Syrgkanis, Matt Taddy

We consider dynamic pricing with many products under an evolving but low-dimensional demand model.

Sequence to Better Sequence: Continuous Revision of Combinatorial Structures

no code implementations ICML 2017 Jonas Mueller, David Gifford, Tommi Jaakkola

Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes.

Learning Optimal Interventions

no code implementations16 Jun 2016 Jonas Mueller, David N. Reshef, George Du, Tommi Jaakkola

Assuming the underlying relationship remains invariant under intervention, we develop efficient algorithms to identify the optimal intervention policy from limited data and provide theoretical guarantees for our approach in a Gaussian Process setting.

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