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1 code implementation • 4 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.

no code implementations • 26 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.

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.).

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.

2 code implementations • 19 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.

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.

2 code implementations • 26 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.

no code implementations • 26 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.

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.

no code implementations • 1 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.

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.

27 code implementations • 19 Apr 2020 • Hang Zhang, Chongruo wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander Smola

It is well known that featuremap attention and multi-path representation are important for visual recognition.

Ranked #5 on Instance Segmentation on COCO test-dev (APS metric)

no code implementations • 6 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.

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.

7 code implementations • 13 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.

no code implementations • 25 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.

no code implementations • 11 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.

no code implementations • 18 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.

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.

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.

1 code implementation • 9 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.

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.

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.

no code implementations • 16 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.

no code implementations • NeurIPS 2015 • Jonas Mueller, Tommi Jaakkola

We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions.

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