Improves flu prediction by blending environment and population info.
problem Challenges in using data from one environment in another due to feature variability and population subgroup differences.
method Population-aware hierarchical Bayesian domain adaptation framework with multiple invariant components.
result Model improves flu prediction in new environments with unlabelled data.
DICE learns population dynamics from discrete samples.
problem Learning smooth population dynamics from discrete data.
method Discrete Inverse Continuity Equation (DICE) method.
result DICE models are stable and generate representative samples.
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when reported in a doctor's office versus from an online app, precluding direct…
We study the problem of designing models for machine learning tasks defined on \emph{sets}. In contrast to traditional approach of operating on fixed dimensional vectors, we consider objective functions defined on sets that are invariant to permutations. Such problems are widespread, ranging from estimation of populati…
Method tackles missing covariates in large-scale datasets.
problem Cross-population missing data problem in large-scale datasets.
method Augmented transfer regression learning method combining importance-weighted estimating equations and imputation terms.
result Estimator is n1/2-consistent and asymptotically normal, attaining semiparametric efficiency bound under correct specification. Adversarial techniques learn invariant representations across multiple domains.
problem Domain generalization from diverse studies to unseen domains.
method Adversarial censoring techniques for invariant representation learning.
result Limiting behavior of adversarial loss function as the number of domains grows.
Study proves higher-order conformal forms don't exist in odd dimensions.
problem Proving non-existence of higher-order conformal forms in odd dimensions.
method Analyzing conformal hypersurface embeddings and differential order invariants.
result General non-existence of higher-order conformal forms in odd dimensions.
An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques…
IRM fails to capture natural invariances on simple problems.
problem IRM fails to capture natural invariances on simple problems.
method IRM formulation and practical linear variant.
result IRM can lead to worse generalization than unconstrained ERM.
DM improves self-supervised transfer learning by matching target distributions.
problem Improving self-supervised transfer learning performance.
method Distribution Matching (DM) method that drives representation distribution towards a predefined reference distribution.
result DM outperforms existing methods on target classification tasks.
New method uses MMD estimators to enforce model invariance with missing data.
problem Models trained on missing data can fail on related test distributions.
method Derives MMD estimators for enforcing model invariance under missing nuisances.
result Optimizing through MMD estimates achieves similar test performance to using full data.
Study connects covariance cleaning theory to information theory for heavy-tailed distributions.
problem Optimizing covariance matrices for heavy-tailed distributions using information theory.
method Minimizing Frobenius norm and information loss between true and estimated covariance matrices.
result Asymptotic regime of large matrices minimizes information loss for Student's t distributions.
This paper develops methods for obtaining distribution-free prediction regions for invariant representations.
problem Distributional shifts in machine learning models.
method Invariant risk minimization and weighted conformity scores.
result Proves the effectiveness of adaptive conformal intervals for uncertainty estimation.
A new model synthesizes population with fewer structural and sampling zeros.
problem Synthesizing a feasible and diverse synthetic population from limited data.
method A deep generative model with two regularizations to minimize structural zeros and preserve sampling zeros.
result The model significantly improves feasibility and diversity of synthetic populations.
Aims to describe neural network training dynamics using two-time-scale models.
problem Lack of a general mathematical description of neural network training.
method Introduces a theoretical framework based on two-time-scale population dynamics.
result Derives selection-mutation equations and effective fitness for hyperparameters.
NeuPL learns diverse policies in strategy games efficiently.
problem Iterative training of policies in strategy games leads to under-trained good-responses and wasteful repetition.
method NeuPL uses a single conditional model to represent a population of policies, offering convergence guarantees and transfer learning.
result NeuPL achieves better performance and efficiency across various domains, enabling access to novel strategies.
Proposes a federated transfer learning method to improve precision medicine models for underrepresented populations.
problem Underrepresentation of minorities in precision medicine research leads to underperforming risk prediction models.
method Two-way federated transfer learning strategy integrating diverse populations and healthcare institutions.
result Improves risk prediction models for underrepresented populations, reducing performance gaps.
New framework for online control in evolving populations.
problem Control of evolving populations in real-world conditions.
method Online control framework for linear and non-linear dynamical systems.
result Near-optimal regret bounds for gradient-based controllers.
Study multiple-population games using McKean-Vlasov equations.
problem Mean field games and control problems with multiple populations.
method Coupled forward-backward SDEs and Pontryagin's principle.
result Existence of mean field equilibria under various cooperation scenarios.
Develops robust learning methods for datasets with sub-populations.
problem Robust performance and generalization to unseen testing populations in datasets with sub-populations.
method Min-max-regret (MMR) formulation for distribution-free robust hierarchical model.
result Empirical MMR enjoys regret guarantees on training and unseen testing populations.
Population-based learning improves representation of unstructured data.
problem Improving representation of unstructured data.
method Instantiating Lewis signaling games within a population of agents.
result Population-based learning produces better representations than single-agent learning.
Estimates Mozambique's population using remote sensing and microcensus data.
problem Lack of frequent population estimation due to censuses lacking spatio-temporal resolution.
method Combines remote sensing, microcensus data, and transfer learning with publicly available datasets.
result Population predictions improve with footprint area estimation using transfer learning.
Study landscape of non-convex empirical risk with degenerate population risk.
problem Degenerate non-convex population risk in machine learning problems.
method Analyze population risk first, then connect to empirical risk landscape.
result Established correspondence between empirical and population risk critical points.
We study existence and uniqueness of continuous-time stochastic Radner equilibria in an incomplete market model among a group of agents whose preference is characterized by cash invariant time-consistent monetary utilities. An assumption of "smallness" type is shown to be sufficient for existence and uniqueness. In par…
Paper develops a two-population model to assess longevity basis risk.
problem Mismatch between hedger's liability and hedging instrument causes longevity basis risk.
method Develops a two-population mortality model using Lee-Carter model and renewal process.
result Proposed model provides significant risk reduction when mortality jumps and sampling risk are considered.
A clustering method for multivariate populations with similar dependence structures.
problem Grouping populations with similar dependence structures.
method Orthogonal projection coefficients of density copulas estimated from populations.
result Clusters of populations with similar dependence structures.
A new data set helps estimate continental-scale population distributions.
problem Lack of comprehensive, publicly available data for population estimation.
method Comprehensive data set combining satellite imagery and open-source data.
result Provides a valuable resource for developing population estimation methods.
This paper studies the sample complexity of searching over multiple populations. We consider a large number of populations, each corresponding to either distribution P0 or P1. The goal of the search problem studied here is to find one population corresponding to distribution P1 with as few samples as possible. The main…
System exposes study population descriptions in clinical guidelines.
problem Challenges in understanding applicability of clinical guidelines.
method Developed an ontology-enabled prototype system using SIO.
result Allows medical practitioners to better understand study populations.
Estimates personalized policies robust to shifts in target populations.
problem Estimating policies that perform well in diverse target populations.
method Develops methods for estimating robust policies considering shifts in outcomes and characteristics.
result Welfare-maximizing policies are robust to certain shifts in potential outcomes.
CrowdLLM uses LLMs and generative models to create diverse digital populations.
problem Lack of diversity and accuracy in digital populations created by LLMs.
method Integrates pretrained LLMs and generative models to enhance diversity and fidelity.
result CrowdLLM achieves promising performance in accuracy and distributional fidelity.
Copula-based method generates synthetic populations from marginal distributions.
problem Generating realistic synthetic populations from limited data.
method Copula-based framework for population synthesis.
result Copula framework enhances transferability and realism of synthetic populations.
Existence of Radner equilibrium proven with growing population.
problem Analyzing Radner equilibrium in a model with population growth.
method Proved existence of equilibrium for growing population using mathematical analysis.
result Equilibrium exists for a growing population, with effects on annuity prices.
CTGAN synthesizes population data for travel behavior simulation.
problem Synthesizing population data for agent-based transportation modeling.
method Composite Travel Generative Adversarial Network (CTGAN).
result Consistent and accurate generation of synthetic populations with tabular and sequential mobility data.
EPC curriculum improves MARL performance as agent population grows.
problem Challenges in learning good policies for large multi-agent systems.
method Evolutionary Population Curriculum (EPC) for scaling MARL.
result EPC consistently outperforms baselines as agent population increases.
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables. The resulting diversity of neural tuning often obscures the represented information. Here we introduce a novel dimensionality reduction technique, demixed principal component analysis (dPC…
Develops a method to estimate personalized treatment regimes from summary statistics.
problem Estimating optimal treatment regimes for a target population when individual-level data is unavailable.
method A weighting framework that tailors a treatment regime for the target population using summary statistics.
result Consistent and asymptotically normal estimator for optimal treatment regimes.
LEARNER improves low-rank matrix estimation using source population data.
problem Improving low-rank matrix estimation in target populations with diverse data sources.
method LEARNER uses similarity in latent spaces between source and target populations to enhance estimation.
result LEARNER often outperforms benchmark methods, especially with higher signal-to-noise ratios in the source population.
Develops a weighting framework to generalize ITRs from source to target populations.
problem Challenges in generalizing ITRs from a source population to a target population with differing characteristics.
method A robust sample weighting framework using a reproducing kernel Hilbert space to balance covariates and improve ITR learning methods.
result Improves ITR estimation for the target population compared to other weighting methods.
With the development of high-throughput technologies, principal component analysis (PCA) in the high-dimensional regime is of great interest. Most of the existing theoretical and methodological results for high-dimensional PCA are based on the spiked population model in which all the population eigenvalues are equal ex…
Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change increment…
Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific …
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinde…
Reproductive success and survival are influenced by wealth in human populations. Wealth is transmitted to offsprings and strategies of transmission vary over time and among populations, the main variation being how equally wealth is transmitted to children. Here we propose a model where we simulate both the dynamics of…
Paper tackles causal effect identification in sub-population with latent variables.
problem Identify causal effects in a sub-population with latent variables.
method Extend relevant graphical definitions and propose an algorithm for the s-ID problem.
result Sound algorithm for s-ID problem with latent variables.
New method learns population dynamics from snapshots, outperforming existing models.
problem Capturing periodic and other dynamical properties of population dynamics.
method Wasserstein Lagrangian Mechanics (WLM) for learning second-order dynamics from observed marginals.
result WLM outperforms existing methods across various dynamics, including vortex dynamics, embryonic development, and flocking.
Estimates KL divergence with fairness considerations for sub-populations.
problem Fairly estimate KL divergence between distributions considering sub-populations.
method Proposes multi-group attribution for KL divergence estimation, derived from multi-calibration.
result Shows multi-group attribution provides better KL divergence estimates conditioned on sub-populations.
Measures of wealth and production have been found to scale superlinearly with the population of a city. Therefore, it makes economic sense for humans to congregate together in dense settlements. A recent model of population dynamics showed that population growth can become superexponential due to the superlinear scalin…