Seth R. Flaxman

According to our database, Seth R. Flaxman authored at least 38 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees.

Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

J. Mach. Learn. Res., 2024

KidSat: satellite imagery to map childhood poverty dataset and benchmark.

Makkunda Sharma, Fan Yang, Duy-Nhat Vo, Esra Suel, Swapnil Mishra, Samir Bhatt, Oliver Fiala, William Rudgard, Seth R. Flaxman

CoRR, 2024

Deep Learning and MCMC with aggVAE for Shifting Administrative Boundaries: Mapping Malaria Prevalence in Kenya.

Elizaveta Semenova, Swapnil Mishra, Samir Bhatt, Seth R. Flaxman, H. Juliette T. Unwin

Proceedings of the Epistemic Uncertainty in Artificial Intelligence, 2024

2023

BART-based inference for Poisson processes.

Stamatina Lamprinakou, Mauricio Barahona, Seth R. Flaxman, Sarah Filippi, Axel Gandy, Emma J. McCoy

Comput. Stat. Data Anal., April, 2023

Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes.

Xenia Miscouridou, Samir Bhatt, George O. Mohler, Seth R. Flaxman, Swapnil Mishra

Trans. Mach. Learn. Res., 2023

PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling.

Elizaveta Semenova, Max Cairney-Leeming, Seth R. Flaxman

CoRR, 2023

Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference.

Giovanni Charles, Timothy M. Wolock, Peter Winskill, Azra C. Ghani, Samir Bhatt, Seth R. Flaxman

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Bayesian Kernel Two-Sample Testing.

Qinyi Zhang, Veit Wild, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic

J. Comput. Graph. Stat., October, 2022

πVAE: a stochastic process prior for Bayesian deep learning with MCMC.

Swapnil Mishra, Seth R. Flaxman, Tresnia Berah, Harrison Zhu, Mikko Pakkanen, Samir Bhatt

Stat. Comput., 2022

City-Wide Perceptions of Neighbourhood Quality using Street View Images.

Emily Muller, Emily Gemmell, Ishmam Choudhury, Ricky Nathvani, Antje Barbara Metzler, James E. Bennett, Emily Denton, Seth R. Flaxman, Majid Ezzati

CoRR, 2022

2021

Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.

Andrew J. Holbrook, Charles E. Loeffler, Seth R. Flaxman, Marc A. Suchard

Stat. Comput., 2021

Using Hawkes Processes to model imported and local malaria cases in near-elimination settings.

H. Juliette T. Unwin, Isobel Routledge, Seth R. Flaxman, Marian-Andrei Rizoiu, Shengjie Lai, Justin Cohen, Daniel J. Weiss, Swapnil Mishra, Samir Bhatt

PLoS Comput. Biol., 2021

Unrepresentative big surveys significantly overestimated US vaccine uptake.

Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth R. Flaxman

Nat., 2021

PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability.

Sílvia Casacuberta, Esra Suel, Seth R. Flaxman

CoRR, 2021

Encoding spatiotemporal priors with VAEs for small-area estimation.

Elizaveta Semenova, Yidan Xu, Adam Howes, Theo Rashid, Samir Bhatt, Swapnil Mishra, Seth R. Flaxman

CoRR, 2021

Gaussian process nowcasting: application to COVID-19 mortality reporting.

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

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020

Improving axial resolution in SIM using deep learning.

Miguel A. Boland, Edward A. K. Cohen, Seth R. Flaxman, Mark A. A. Neil

CoRR, 2020

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

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

CoRR, 2020

πVAE: Encoding stochastic process priors with variational autoencoders.

Swapnil Mishra, Seth R. Flaxman, Samir Bhatt

CoRR, 2020

Bayesian Probabilistic Numerical Integration with Tree-Based Models.

Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Modeling and Forecasting Art Movements with CGANs.

Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi, F. Din-Houn Lau, Seth R. Flaxman

CoRR, 2019

Interpreting Deep Neural Networks Through Variable Importance.

Jonathan Ish-Horowicz, Dana Udwin, Seth R. Flaxman, Sarah Filippi, Lorin Crawford

CoRR, 2019

2018

Variational Learning on Aggregate Outputs with Gaussian Processes.

Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Multimodal Sentiment Analysis To Explore the Structure of Emotions.

Anthony Hu, Seth R. Flaxman

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Bayesian Approaches to Distribution Regression.

Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling.

Gabriele Abbati, Alessandra Tosi, Michael A. Osborne, Seth R. Flaxman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Bayesian Distribution Regression.

Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman

CoRR, 2017

European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation".

Bryce Goodman, Seth R. Flaxman

AI Mag., 2017

Feature-to-Feature Regression for a Two-Step Conditional Independence Test.

Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Poisson intensity estimation with reproducing kernels.

Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference.

Seth R. Flaxman, Daniel B. Neill, Alexander J. Smola

ACM Trans. Intell. Syst. Technol., 2016

Tucker Gaussian Process for Regression and Collaborative Filtering.

Hyunjik Kim, Xiaoyu Lu, Seth R. Flaxman, Yee Whye Teh

CoRR, 2016

EU regulations on algorithmic decision-making and a "right to explanation".

Bryce Goodman, Seth R. Flaxman

CoRR, 2016

Bayesian Learning of Kernel Embeddings.

Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces.

William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Who Supported Obama in 2012?: Ecological Inference through Distribution Regression.

Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods.

Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola

Proceedings of the 32nd International Conference on Machine Learning, 2015

2009

Approximation Algorithms for Traffic Grooming in WDM Rings.

Kevin Corcoran, Seth R. Flaxman, Mark Neyer, Peter Scherpelz, Craig Weidert, Ran Libeskind-Hadas

Proceedings of IEEE International Conference on Communications, 2009