arXiv research

Method predicts computational reproducibility of large population studies data analysis pipelines.

System exposes study population descriptions in clinical guidelines.

Study equilibrium consumption habits in a large population using mean field games.

Study shows neural networks outperform traditional methods in speaker identification.

Study multiple-population games using McKean-Vlasov equations.

Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which assume that we condition on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze strea…

CrowdLLM uses LLMs and generative models to create diverse digital populations.

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…

Study uses MFG approach to model equilibrium pricing with market clearing condition.

Framework generates precise synthetic populations for scalable modeling.

Method tackles missing covariates in large-scale datasets.

Infinitesimal boosting converges to a deterministic process in large sample limit.

Proposes a federated transfer learning method to improve precision medicine models for underrepresented populations.

Aims to describe neural network training dynamics using two-time-scale models.

Paper studies optimal tracking portfolio in mean field game of large fund competition.

EPC curriculum improves MARL performance as agent population grows.

In lowest unique bid auctions, $N$ players bid for an item. The winner is whoever places the \emph{lowest} bid, provided that it is also unique. We use a grand canonical approach to derive an analytical expression for the equilibrium distribution of strategies. We then study the properties of the solution as a function…

PAVI speeds up VI for large-scale studies by sharing parameterization across i.i.d. variables.

The paper proposes a new method for comparing logistic regression models across different populations.

New model estimates species population trends from citizen science data.

Study on order book dynamics with uniform catastrophes, explaining volatility and trends.

A game environment simulates competition among many agents for resources.

Study laws of large numbers in online classification, determining optimal regret bounds.

PAVI speeds up Bayesian inference for large datasets.

We study the top-$K$ ranking problem where the goal is to recover the set of top-$K$ ranked items out of a large collection of items based on partially revealed preferences. We consider an adversarial crowdsourced setting where there are two population sets, and pairwise comparison samples drawn from one of the populat…

NPE trains neural networks to approximate posterior distributions in SIR models from final outcome data.

New method pools labels from similar data items to improve learning from small samples.

Study evaluates MRIQC pipeline's generalization on large multi-center datasets.

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network i…

Study shows finite agent equilibrium converges to mean-field limit in asset pricing.

A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedde…

A new method simulates large, diverse populations of learning agents evolving in games.

Study proves convergence of subgradients for optimal transport-based objectives.

Estimates population parameters from limited trials per individual.

Improves flu prediction by blending environment and population info.

In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related can…

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…

Fiber simplifies RL and population-based methods for distributed training.

TMLE improves IPM estimation for ecological population dynamics.

Study on signal recovery from low-rank matrix with sparse noise.

In stochastic optimization, the population risk is generally approximated by the empirical risk. However, in the large-scale setting, minimization of the empirical risk may be computationally restrictive. In this paper, we design an efficient algorithm to approximate the population risk minimizer in generalized linear …

Study explores fairness in loan decisions using dynamic modeling.

Model captures context-dependent neural correlations using Poisson mixtures.

Insiders camouflage trading to balance wealth and stealth, avoiding legal penalties.

In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While c…

Consider the problem of finding a population or a probability distribution amongst many with the largest mean when these means are unknown but population samples can be simulated or otherwise generated. Typically, by selecting largest sample mean population, it can be shown that false selection probability decays at an…

Paper uses neural networks to calibrate Lee-Carter models for multiple populations.

The study of networks leads to a wide range of high dimensional inference problems. In many practical applications, one needs to draw inference from one or few large sparse networks. The present paper studies hypothesis testing of graphs in this high-dimensional regime, where the goal is to test between two populations…