Author name cluster

Seth Flaxman

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

TMLR Journal 2026 Journal Article

Benchmarking Missing Data Imputation Methods in Socioeconomic Surveys

Siyi Sun
David Antony Selby
Yunchuan Huang
Ayush Patnaik
Sebastian Josef Vollmer
Seth Flaxman
Anisoara Calinescu

Missing data imputation is a core challenge in socioeconomic surveys, where data is often longitudinal, hierarchical, high-dimensional, not independent and identically distributed, and missing under complex mechanisms. Socioeconomic datasets like the Consumer Pyramids Household Survey (CPHS)-the largest continuous household survey in India since 2014, covering 174,000 households-highlight the importance of robust imputation, which can reduce survey costs, preserve statistical power, and enable timely policy analysis. This paper systematically evaluates these methods under three missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), across five missingness ratios ranging from 10% to 50%. We evaluate imputation performance on both continuous and categorical variables, assess the impact on downstream tasks, and compare the computational efficiency of each method. Our results indicate that classical machine learning methods such as MissForest and HyperImpute remain strong baselines with favorable trade-offs between accuracy and efficiency, while deep learning methods perform better under complex missingness patterns and higher missingness ratios, but face scalability challenges. We ran experiments on CPHS and multiple synthetic survey datasets, and found consistent patterns across them. Our framework aims to provide a reliable benchmark for structured socioeconomic surveys, and addresses the critical gap in reproducible, domain-specific evaluation of imputation methods. The open-source code is provided.

PDF Details

JMLR Journal 2024 Journal Article

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

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

Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent Gaussian models. Within a system, the Gaussian process model needs to perform in a stable and reliable manner to ensure it interacts correctly with other parts of the system. In this work, we study the numerical stability of scalable sparse approximations based on inducing points. To do so, we first review numerical stability, and illustrate typical situations in which Gaussian process models can be unstable. Building on stability theory originally developed in the interpolation literature, we derive sufficient and in certain cases necessary conditions on the inducing points for the computations performed to be numerically stable. For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions. This is done via a modification of the cover tree data structure, which is of independent interest. We additionally propose an alternative sparse approximation for regression with a Gaussian likelihood which trades off a small amount of performance to further improve stability. We provide illustrative examples showing the relationship between stability of calculations and predictive performance of inducing point methods on spatial tasks. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

PDF Details

TMLR Journal 2023 Journal Article

Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes

Xenia Miscouridou
Samir Bhatt
George Mohler
Seth Flaxman
Swapnil Mishra

Hawkes processes are point process models that have been used to capture self-excitatory behaviour in social interactions, neural activity, earthquakes and viral epidemics. They can model the occurrence of the times and locations of events. Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behaviour and we provide an efficient method for performing inference. We use a log-Gaussian Cox process (LGCP) as prior for the background rate of the Hawkes process which gives arbitrary flexibility to capture a wide range of underlying background effects (for infectious diseases these are called endemic effects). The Hawkes process and LGCP are computationally expensive due to the former having a likelihood with quadratic complexity in the number of observations and the latter involving inversion of the precision matrix which is cubic in observations. Here we propose a novel approach to perform MCMC sampling for our Hawkes process with LGCP background, using pre-trained Gaussian Process generators which provide direct and cheap access to samples during inference. We show the efficacy and flexibility of our approach in experiments on simulated data and use our methods to uncover the trends in a dataset of reported crimes in the US.

PDF Details

AAAI Conference 2023 Conference Paper

Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference

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

Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference.

PDF Details DOI

NeurIPS Conference 2020 Conference Paper

Bayesian Probabilistic Numerical Integration with Tree-Based Models

Harrison Zhu
Xing Liu
Ruya Kang
Zhichao Shen
Seth Flaxman
Francois-Xavier Briol

Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows users to quantify their uncertainty about the solution. The standard approach to BQ is based on a Gaussian process (GP) approximation of the integrand. As a result, BQ is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting very high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which we call BART-Int. BART priors are easy to tune and well-suited for discontinuous functions. We demonstrate that they also lend themselves naturally to a sequential design setting and that explicit convergence rates can be obtained in a variety of settings. The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, on a rare-event simulation problem and on a Bayesian survey design problem.

PDF Details

NeurIPS Conference 2018 Conference Paper

Variational Learning on Aggregate Outputs with Gaussian Processes

Ho Chung Law
Dino Sejdinovic
Ewan Cameron
Tim 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. Aggregation of outputs makes generalization to new inputs much more difficult. We consider an approach to this problem based on variational learning with a model of output aggregation and Gaussian processes, where aggregation leads to intractability of the standard evidence lower bounds. We propose new bounds and tractable approximations, leading to improved prediction accuracy and scalability to large datasets, while explicitly taking uncertainty into account. We develop a framework which extends to several types of likelihoods, including the Poisson model for aggregated count data. We apply our framework to a challenging and important problem, the fine-scale spatial modelling of malaria incidence, with over 1 million observations.

PDF Details