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ORCIDSeth R. Flaxman
Person information
- affiliation: University of Oxford, Department of Computer Science, Oxford, UK
- affiliation: Imperial College London, Department of Mathematics, London, UK
- affiliation: University of Oxford, Department of Statistics, Oxford, UK
- affiliation: Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
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2020 – today
- 2025
[j11]Moritz U. G. Kraemer, Joseph L.-H. Tsui, Serina Chang, Spyros Lytras, Mark P. Khurana, Samantha Vanderslott, Sumali Bajaj, Neil Scheidwasser, Jacob Liam Curran-Sebastian, Elizaveta Semenova, Mengyan Zhang, H. Juliette T. Unwin, Oliver J. Watson, Cathal Mills, Abhishek Dasgupta, Luca Ferretti, Samuel V. Scarpino, Etien Koua, Oliver Morgan, Houriiyah Tegally, Ulrich Paquet, Loukas Moutsianas, Christophe Fraser, Neil M. Ferguson, Eric J. Topol, David A. Duchêne, Tanja Stadler, Patricia Kingori, Michael J. Parker, Francesca Dominici, Nigel Shadbolt, Marc A. Suchard, Oliver Ratmann, Seth R. Flaxman, Edward C. Holmes, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Christl A. Donnelly, Oliver G. Pybus, Simon Cauchemez, Samir Bhatt:
Artificial intelligence for modelling infectious disease epidemics. Nat. 638(8051): 623-635 (2025)- [i28]Jhonathan Navott, Daniel Jenson, Seth R. Flaxman, Elizaveta Semenova:
DeepRV: pre-trained spatial priors for accelerated disease mapping. CoRR abs/2503.21473 (2025) - [i27]Siyi Sun, David Antony Selby, Yunchuan Huang, Sebastian J. Vollmer, Seth R. Flaxman, Anisoara Calinescu:
IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples). CoRR abs/2506.08844 (2025) - [i26]Daniel Jenson, Jhonathan Navott, Piotr Grynfelder, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, Seth R. Flaxman:
Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes. CoRR abs/2506.09163 (2025) - 2024
- [j10]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. 25: 26:1-26:36 (2024) - [c15]Elizaveta Semenova
, Swapnil Mishra, Samir Bhatt, Seth R. Flaxman, H. Juliette T. Unwin:
Deep Learning and MCMC with aggVAE for Shifting Administrative Boundaries: Mapping Malaria Prevalence in Kenya. Epi UAI 2024: 13-27 - [i25]Makkunda Sharma, Fan Yang, Duy-Nhat Vo, Esra Suel, Swapnil Mishra, Samir Bhatt, Oliver Fiala, William Rudgard, Seth R. Flaxman:
KidSat: satellite imagery to map childhood poverty dataset and benchmark. CoRR abs/2407.05986 (2024) - [i24]Sumantrak Mukherjee, Mengyan Zhang, Seth R. Flaxman, Sebastian J. Vollmer:
Graph Agnostic Causal Bayesian Optimisation. CoRR abs/2411.03028 (2024) - [i23]Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, Seth R. Flaxman:
Transformer Neural Processes - Kernel Regression. CoRR abs/2411.12502 (2024) - [i22]Fan Yang, Sahoko Ishida, Mengyan Zhang, Daniel Jenson, Swapnil Mishra, Jhonathan Navott, Seth R. Flaxman:
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing. CoRR abs/2411.14119 (2024) - [i21]Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong, Seth R. Flaxman, Dino Sejdinovic:
Indirect Query Bayesian Optimization with Integrated Feedback. CoRR abs/2412.13559 (2024) - 2023
- [j9]Stamatina Lamprinakou, Mauricio Barahona, Seth R. Flaxman, Sarah Filippi, Axel Gandy, Emma J. McCoy:
BART-based inference for Poisson processes. Comput. Stat. Data Anal. 180: 107658 (2023) - [j8]Xenia Miscouridou, Samir Bhatt, George O. Mohler, Seth R. Flaxman, Swapnil Mishra:
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes. Trans. Mach. Learn. Res. 2023 (2023) - [c14]Giovanni Charles, Timothy M. Wolock, Peter Winskill, Azra C. Ghani, Samir Bhatt, Seth R. Flaxman:
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. AAAI 2023: 14170-14177 - [i20]Elizaveta Semenova, Max Cairney-Leeming, Seth R. Flaxman:
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling. CoRR abs/2304.04307 (2023) - [i19]Elizaveta Semenova, Swapnil Mishra, Samir Bhatt, Seth R. Flaxman, H. Juliette T. Unwin:
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya. CoRR abs/2305.19779 (2023) - 2022
- [j7]Qinyi Zhang, Veit Wild, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic:
Bayesian Kernel Two-Sample Testing. J. Comput. Graph. Stat. 31(4): 1164-1176 (2022) - [j6]Swapnil Mishra, Seth R. Flaxman, Tresnia Berah, Harrison Zhu, Mikko Pakkanen, Samir Bhatt:
πVAE: a stochastic process prior for Bayesian deep learning with MCMC. Stat. Comput. 32(6): 96 (2022) - [i18]Giovanni Charles, Timothy M. Wolock, Peter Winskill, Azra C. Ghani, Samir Bhatt, Seth R. Flaxman:
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. CoRR abs/2209.09617 (2022) - [i17]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR abs/2210.07893 (2022) - [i16]Xenia Miscouridou, Samir Bhatt, George O. Mohler, Seth R. Flaxman, Swapnil Mishra:
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes. CoRR abs/2210.11844 (2022) - [i15]Emily Muller, Emily Gemmell, Ishmam Choudhury, Ricky Nathvani, Antje Barbara Metzler, James E. Bennett, Emily Denton, Seth R. Flaxman, Majid Ezzati:
City-Wide Perceptions of Neighbourhood Quality using Street View Images. CoRR abs/2211.12139 (2022) - 2021
- [j5]Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth R. Flaxman:
Unrepresentative big surveys significantly overestimated US vaccine uptake. Nat. 600(7890): 695-700 (2021) - [j4]H. Juliette T. Unwin, Isobel Routledge, Seth R. Flaxman, Marian-Andrei Rizoiu, Shengjie Lai, Justin Cohen, Daniel J. Weiss, Swapnil Mishra, Samir Bhatt:
Using Hawkes Processes to model imported and local malaria cases in near-elimination settings. PLoS Comput. Biol. 17(4) (2021) - [j3]Andrew J. Holbrook, Charles E. Loeffler, Seth R. Flaxman, Marc A. Suchard:
Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data. Stat. Comput. 31(1): 4 (2021) - [c13]Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P. Schnekenberg, Charles Whittaker, Michaela A. C. Vollmer, Seth R. Flaxman, Samir Bhatt, Thomas A. Mellan:
Gaussian process nowcasting: application to COVID-19 mortality reporting. UAI 2021: 1258-1268 - [i14]Elizaveta Semenova, Yidan Xu, Adam Howes, Theo Rashid, Samir Bhatt, Swapnil Mishra, Seth R. Flaxman:
Encoding spatiotemporal priors with VAEs for small-area estimation. CoRR abs/2110.10422 (2021) - [i13]Sílvia Casacuberta, Esra Suel, Seth R. Flaxman:
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability. CoRR abs/2112.15571 (2021) - 2020
- [c12]Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol:
Bayesian Probabilistic Numerical Integration with Tree-Based Models. NeurIPS 2020 - [i12]Swapnil Mishra, Seth R. Flaxman, Samir Bhatt:
πVAE: Encoding stochastic process priors with variational autoencoders. CoRR abs/2002.06873 (2020) - [i11]Stamatina Lamprinakou, Emma J. McCoy, Mauricio Barahona, Axel Gandy, Seth R. Flaxman, Sarah Filippi:
BART-based inference for Poisson processes. CoRR abs/2005.07927 (2020) - [i10]Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol:
Bayesian Probabilistic Numerical Integration with Tree-Based Models. CoRR abs/2006.05371 (2020) - [i9]Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costelloe, Robert Klaber, Graham S. Cooke, Seth R. Flaxman, Samir Bhatt:
A unified machine learning approach to time series forecasting applied to demand at emergency departments. CoRR abs/2007.06566 (2020) - [i8]Miguel A. Boland, Edward A. K. Cohen, Seth R. Flaxman, Mark A. A. Neil:
Improving axial resolution in SIM using deep learning. CoRR abs/2009.02264 (2020)
2010 – 2019
- 2019
- [i7]Jonathan Ish-Horowicz, Dana Udwin, Seth R. Flaxman, Sarah Filippi, Lorin Crawford:
Interpreting Deep Neural Networks Through Variable Importance. CoRR abs/1901.09839 (2019) - [i6]Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi, F. Din-Houn Lau, Seth R. Flaxman:
Modeling and Forecasting Art Movements with CGANs. CoRR abs/1906.09230 (2019) - 2018
- [c11]Gabriele Abbati, Alessandra Tosi, Michael A. Osborne, Seth R. Flaxman:
AdaGeo: Adaptive Geometric Learning for Optimization and Sampling. AISTATS 2018: 226-234 - [c10]Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman:
Bayesian Approaches to Distribution Regression. AISTATS 2018: 1167-1176 - [c9]Anthony Hu, Seth R. Flaxman:
Multimodal Sentiment Analysis To Explore the Structure of Emotions. KDD 2018: 350-358 - [c8]Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. NeurIPS 2018: 6084-6094 - [i5]Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. CoRR abs/1805.08463 (2018) - [i4]Anthony Hu, Seth R. Flaxman:
Multimodal Sentiment Analysis To Explore the Structure of Emotions. CoRR abs/1805.10205 (2018) - 2017
- [j2]Bryce Goodman, Seth R. Flaxman:
European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation". AI Mag. 38(3): 50-57 (2017) - [c7]Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic:
Poisson intensity estimation with reproducing kernels. AISTATS 2017: 270-279 - [c6]Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic:
Feature-to-Feature Regression for a Two-Step Conditional Independence Test. UAI 2017 - [i3]Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman:
Bayesian Distribution Regression. CoRR abs/1705.04293 (2017) - 2016
- [j1]Seth R. Flaxman, Daniel B. Neill, Alexander J. Smola:
Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference. ACM Trans. Intell. Syst. Technol. 7(2): 22:1-22:23 (2016) - [c5]William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing:
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. AISTATS 2016: 1013-1021 - [c4]Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi:
Bayesian Learning of Kernel Embeddings. UAI 2016 - [i2]Hyunjik Kim, Xiaoyu Lu, Seth R. Flaxman, Yee Whye Teh:
Tucker Gaussian Process for Regression and Collaborative Filtering. CoRR abs/1605.07025 (2016) - [i1]Bryce Goodman, Seth R. Flaxman:
EU regulations on algorithmic decision-making and a "right to explanation". CoRR abs/1606.08813 (2016) - 2015
- [c3]Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola:
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. ICML 2015: 607-616 - [c2]Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola:
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. KDD 2015: 289-298
2000 – 2009
- 2009
- [c1]Kevin Corcoran, Seth R. Flaxman, Mark Neyer, Peter Scherpelz, Craig Weidert, Ran Libeskind-Hadas:
Approximation Algorithms for Traffic Grooming in WDM Rings. ICC 2009: 1-6
Coauthor Index
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