Seth R. Flaxman

Orcid: 0000-0002-2477-4217

Affiliations:
  • Imperial College London, Department of Mathematics, UK
  • University of Oxford, Department of Statistics, UK
  • Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA


According to our database1, Seth R. Flaxman authored at least 38 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Online presence:

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Bibliography

2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees.
J. Mach. Learn. Res., 2024

KidSat: satellite imagery to map childhood poverty dataset and benchmark.
CoRR, 2024

Deep Learning and MCMC with aggVAE for Shifting Administrative Boundaries: Mapping Malaria Prevalence in Kenya.
Proceedings of the Epistemic Uncertainty in Artificial Intelligence, 2024

2023
BART-based inference for Poisson processes.
Comput. Stat. Data Anal., April, 2023

Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes.
Trans. Mach. Learn. Res., 2023

PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling.
CoRR, 2023

Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Bayesian Kernel Two-Sample Testing.
J. Comput. Graph. Stat., October, 2022

πVAE: a stochastic process prior for Bayesian deep learning with MCMC.
Stat. Comput., 2022

City-Wide Perceptions of Neighbourhood Quality using Street View Images.
CoRR, 2022

2021
Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.
Stat. Comput., 2021

Using Hawkes Processes to model imported and local malaria cases in near-elimination settings.
PLoS Comput. Biol., 2021

Unrepresentative big surveys significantly overestimated US vaccine uptake.
Nat., 2021

PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability.
CoRR, 2021

Encoding spatiotemporal priors with VAEs for small-area estimation.
CoRR, 2021

Gaussian process nowcasting: application to COVID-19 mortality reporting.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020
Improving axial resolution in SIM using deep learning.
CoRR, 2020

A unified machine learning approach to time series forecasting applied to demand at emergency departments.
CoRR, 2020

πVAE: Encoding stochastic process priors with variational autoencoders.
CoRR, 2020

Bayesian Probabilistic Numerical Integration with Tree-Based Models.
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.
CoRR, 2019

Interpreting Deep Neural Networks Through Variable Importance.
CoRR, 2019

2018
Variational Learning on Aggregate Outputs with Gaussian Processes.
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.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Bayesian Approaches to Distribution Regression.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Bayesian Distribution Regression.
CoRR, 2017

European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation".
AI Mag., 2017

Feature-to-Feature Regression for a Two-Step Conditional Independence Test.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Poisson intensity estimation with reproducing kernels.
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.
ACM Trans. Intell. Syst. Technol., 2016

Tucker Gaussian Process for Regression and Collaborative Filtering.
CoRR, 2016

EU regulations on algorithmic decision-making and a "right to explanation".
CoRR, 2016

Bayesian Learning of Kernel Embeddings.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression.
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.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2009
Approximation Algorithms for Traffic Grooming in WDM Rings.
Proceedings of IEEE International Conference on Communications, 2009


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