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ORCIDSeth R. Flaxman
Person information
- affiliation: Imperial College London, Department of Mathematics, UK
- affiliation: University of Oxford, Department of Statistics, UK
- affiliation: Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
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2020 – today
- 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 - [i21]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) - 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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last updated on 2024-10-07 21:14 CEST by the dblp team
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