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Graph Information Bottleneck

Oct 24, 2020
Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec

* 20 pages, 3 figures, NeurIPS 2020 

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AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

Jun 18, 2020
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

* 16 pages, 6 figs 

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Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning

Jan 20, 2020
Tailin Wu

* PhD Thesis, 352 pages. Reference improved 

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Discovering Nonlinear Relations with Minimum Predictive Information Regularization

Jan 07, 2020
Tailin Wu, Thomas Breuel, Michael Skuhersky, Jan Kautz

* 26 pages, 11 figures; ICML'19 Time Series Workshop 

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Phase Transitions for the Information Bottleneck in Representation Learning

Jan 07, 2020
Tailin Wu, Ian Fischer

* ICLR 2020; 27 pages, 7 figures 

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Pareto-optimal data compression for binary classification tasks

Aug 23, 2019
Max Tegmark, Tailin Wu

* 15 pages, 8 figs 

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Learnability for the Information Bottleneck

Jul 17, 2019
Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark

* Accepted at UAI 2019 

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Toward an AI Physicist for Unsupervised Learning

Nov 05, 2018
Tailin Wu, Max Tegmark

* Typos fixed, references added, discussion improved. 18 pages, 7 figs 

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Meta-learning autoencoders for few-shot prediction

Jul 26, 2018
Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark


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Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Aug 09, 2017
Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang


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