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Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting


Feb 22, 2021
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P Schnekenberg, Charles Whittaker, Michaela Vollmer, Seth Flaxman, Samir Bhatt, Thomas A Mellan

* 27 pages, 25 figures 

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Simulating normalising constants with referenced thermodynamic integration: application to COVID-19 model selection


Sep 10, 2020
Iwona Hawryluk, Swapnil Mishra, Seth Flaxman, Samir Bhatt, Thomas A. Mellan

* 27 pages, 8 figures, 3 tables 

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Improving axial resolution in SIM using deep learning


Sep 04, 2020
Miguel Boland, Edward A. K. Cohen, Seth Flaxman, Mark A. A. Neil


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A unified machine learning approach to time series forecasting applied to demand at emergency departments


Jul 13, 2020
Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costello, Robert Klaber, Graham Cooke, Seth Flaxman, Samir Bhatt


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Bayesian Probabilistic Numerical Integration with Tree-Based Models


Jun 09, 2020
Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, François-Xavier Briol


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BART-based inference for Poisson processes


May 16, 2020
Stamatina Lamprinakou, Emma McCoy, Mauricio Barahona, Axel Gandy, Seth Flaxman, Sarah Filippi


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$π$VAE: Encoding stochastic process priors with variational autoencoders


Feb 17, 2020
Swapnil Mishra, Seth Flaxman, Samir Bhatt


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Modeling and Forecasting Art Movements with CGANs


Jun 21, 2019
Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi, F. Din-Houn Lau, Seth Flaxman

* 15 pages, 6 figures 

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Interpreting Deep Neural Networks Through Variable Importance


Jan 28, 2019
Jonathan Ish-Horowicz, Dana Udwin, Seth Flaxman, Sarah Filippi, Lorin Crawford


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Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"


Jul 04, 2018
Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler


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Multimodal Sentiment Analysis To Explore the Structure of Emotions


May 25, 2018
Anthony Hu, Seth Flaxman

* Accepted as a conference paper at KDD 2018 

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Variational Learning on Aggregate Outputs with Gaussian Processes


May 22, 2018
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu


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Bayesian Approaches to Distribution Regression


Feb 22, 2018
Ho Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic, Seth Flaxman

* Final version to be published at AISTATS 2018 

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Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features


Nov 15, 2017
Jean-Francois Ton, Seth Flaxman, Dino Sejdinovic, Samir Bhatt

* under submission to Spatial Statistics Journal 

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Poisson intensity estimation with reproducing kernels


Jun 26, 2017
Seth Flaxman, Yee Whye Teh, Dino Sejdinovic

* AISTATS 2017 

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Collaborative Filtering with Side Information: a Gaussian Process Perspective


Jun 08, 2017
Hyunjik Kim, Xiaoyu Lu, Seth Flaxman, Yee Whye Teh


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Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata


Nov 11, 2016
Seth Flaxman, Dougal Sutherland, Yu-Xiang Wang, Yee Whye Teh


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European Union regulations on algorithmic decision-making and a "right to explanation"


Aug 31, 2016
Bryce Goodman, Seth Flaxman

* AI Magazine, Vol 38, No 3, 2017 
* presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY 

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Bayesian Learning of Kernel Embeddings


Jun 02, 2016
Seth Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi

* Conference paper appearing in UAI 2016, including Appendix 

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Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces


Nov 13, 2015
William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing

* 18 pages, 8 figures 

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