arXiv research

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

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

Machine learning has been an emerging tool for various aspects of infectious diseases including tuberculosis surveillance and detection. However, WHO provided no recommendations on using computer-aided tuberculosis detection software because of the small number of studies, methodological limitations, and limited genera…

Kernelized Taylor diagram visualizes data populations with fewer assumptions.

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…

In this work, we systematically investigate mean field games and mean field type control problems with multiple populations using a coupled system of forward-backward stochastic differential equations of McKean-Vlasov type stemming from Pontryagin's stochastic maximum principle. Although the same cost functions as well…

Paper tackles efficient risk estimation under dataset shift conditions.

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.

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

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

This paper addresses external validity bias in causal inference.

The paper analyzes the maximum margin algorithm's performance on noisy data.

ScoreFusion fuses multiple diffusion models to enhance generative modeling of a target population.

We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution o…

Study controlled contagion with state-dependent killing, proving a comparison principle.

Machine learning's predictive power is limited by sample size, as shown by the Limits-to-Learning Gap.

Copula-based method generates synthetic populations from marginal distributions.

Proposes CRA framework for certifying fair predictive models.

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…

MFM integrates multiple evolving populations using Wasserstein manifold flows.

CLSB models system dynamics from cross-sectional data with population-level regularization.

Develops an equilibrium model for securities pricing in a mixed cooperative and non-cooperative market.

Study on the limits of learning HMM parameters under various conditions.

New method estimates treatment effects across different populations.

Policy mirror ascent achieves Nash equilibrium in mean field games without a population generative model.

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

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

New method finds minimum in noisy data, useful for model selection.

Novel approach for robust domain generalization in health studies.

U-aggregation combines multiple models without labels for better risk prediction.

Polyak step size GD reaches final radius of convergence after log iterations.

Study on optimal trading in a finite population with market frictions and asymmetric information.

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

Among the central tenets of globalization is free migration of labor. Although much has been written about its benefits, little is known about the limitations of globalization, including how immigration affects the anti-globalist sentiment. Analyzing polls data, we find that over the last three years in a group of EU c…

Study of entropy-regularized LQG MFGs with exploratory actions.

This work shifts focus from prediction to intervention in social systems.

Semi-supervised learning benefits the rich more than the poor, affecting fairness.

Operator calculus for population-based optimization provides a unified framework for analyzing convergence of various methods.

This paper highlights the size-dependency of income distributions, i.e. the income distribution curves versus the population of a country systematically. By using the generalized Lotka-Volterra model to fit the empirical income data in the United States during 1996-2007, we found an important parameter $λ$ can scale wi…

The paper develops methods to identify stable associations across multiple studies.

With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accura…

RKD improves clustering in semi-supervised learning with limited labels.

Kernel testing compares cell states in single-cell data.

One shirt size cannot fit everybody, while we cannot make a unique shirt that fits perfectly for everyone because of resource limitation. This analogy is true for the policy making. Policy makers cannot establish a single policy to solve all problems for all regions because each region has its own unique issue. In the …

Improved covariance matrix estimation for multiple classes with limited data.

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

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns i…