Search Results for author: Seth Flaxman

Found 24 papers, 15 papers with code

Encoding spatiotemporal priors with VAEs for small-area estimation

no code implementations20 Oct 2021 Elizaveta Semenova, Yidan Xu, Adam Howes, Theo Rashid, Samir Bhatt, Swapnil Mishra, Seth Flaxman

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatiotemporal statistical modelling.

Gaussian Processes

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

1 code implementation22 Feb 2021 Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P Schnekenberg, Charles Whittaker, Michaela Vollmer, Seth Flaxman, Samir Bhatt, Thomas A Mellan

An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty?

Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection

1 code implementation8 Sep 2020 Iwona Hawryluk, Swapnil Mishra, Seth Flaxman, Samir Bhatt, Thomas A. Mellan

The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.

Epidemiology Model Selection

Improving axial resolution in SIM using deep learning

1 code implementation4 Sep 2020 Miguel Boland, Edward A. K. Cohen, Seth Flaxman, Mark A. A. Neil

Structured Illumination Microscopy is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy.

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

no code implementations13 Jul 2020 Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costello, Robert Klaber, Graham Cooke, Seth Flaxman, Samir Bhatt

We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE.

Time Series Time Series Forecasting

Bayesian Probabilistic Numerical Integration with Tree-Based Models

1 code implementation NeurIPS 2020 Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, François-Xavier Briol

The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.

Numerical Integration

BART-based inference for Poisson processes

no code implementations16 May 2020 Stamatina Lamprinakou, Emma McCoy, Mauricio Barahona, Axel Gandy, Seth Flaxman, Sarah Filippi

The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non parametric regression and classification.

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

1 code implementation17 Feb 2020 Swapnil Mishra, Seth Flaxman, Tresnia Berah, Mikko Pakkanen, Harrison Zhu, Samir Bhatt

We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process).

Gaussian Processes Probabilistic Programming +1

Modeling and Forecasting Art Movements with CGANs

1 code implementation21 Jun 2019 Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi, F. Din-Houn Lau, Seth Flaxman

Realisations from this distribution can be used by the CGAN to generate "future" paintings.

Interpreting Deep Neural Networks Through Variable Importance

1 code implementation28 Jan 2019 Jonathan Ish-Horowicz, Dana Udwin, Seth Flaxman, Sarah Filippi, Lorin Crawford

While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited.

Multimodal Sentiment Analysis To Explore the Structure of Emotions

2 code implementations ICLR 2018 Anthony Hu, Seth Flaxman

We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing.

Multimodal Sentiment Analysis

Variational Learning on Aggregate Outputs with Gaussian Processes

1 code implementation NeurIPS 2018 Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs.

Gaussian Processes

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

1 code implementation9 Jan 2018 Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler

For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods.

Density Estimation Gaussian Processes

Bayesian Approaches to Distribution Regression

1 code implementation11 May 2017 Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth Flaxman

Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level.

Detecting causal associations in large nonlinear time series datasets

2 code implementations22 Feb 2017 Jakob Runge, Dino Sejdinovic, Seth Flaxman

Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain.

Methodology Atmospheric and Oceanic Physics Applications

Is Gun Violence Contagious?

1 code implementation21 Nov 2016 Charles Loeffler, Seth Flaxman

Existing theories of gun violence predict stable spatial concentrations and contagious diffusion of gun violence into surrounding areas.

Applications

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

1 code implementation11 Nov 2016 Seth Flaxman, Danica J. Sutherland, Yu-Xiang Wang, Yee Whye Teh

We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election.

Poisson intensity estimation with reproducing kernels

no code implementations27 Oct 2016 Seth Flaxman, Yee Whye Teh, Dino Sejdinovic

However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite-dimensional problem.

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

no code implementations28 Jun 2016 Bryce Goodman, Seth Flaxman

We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine learning algorithms.

Decision Making

Collaborative Filtering with Side Information: a Gaussian Process Perspective

no code implementations23 May 2016 Hyunjik Kim, Xiaoyu Lu, Seth Flaxman, Yee Whye Teh

We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression.

Collaborative Filtering

Bayesian Learning of Kernel Embeddings

no code implementations7 Mar 2016 Seth Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi

The posterior mean of our model is closely related to recently proposed shrinkage estimators for kernel mean embeddings, while the posterior uncertainty is a new, interesting feature with various possible applications.

Bayesian Inference

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

no code implementations13 Nov 2015 William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure.

Gaussian Processes

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