arXiv research

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

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

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…

A clustering method for multivariate populations with similar dependence structures.

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id…

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

Paper proposes a new method for population-wise matching of sulcal graphs.

In this work, we consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Examples of such data include brain connectivity networks from fMRI flow data, or word co-occurrence counts for populations of individuals. Current approaches to hypothesis testing for weighted n…

Copula-based method generates synthetic populations from marginal distributions.

Develops a method to estimate personalized treatment regimes from summary statistics.

Efficiently estimates variable importance in prediction tasks using Shapley values.

Estimates population mean from user-level data with privacy, accounting for heterogeneity.

Synthetic population generation is the process of combining multiple socioeconomic and demographic datasets from different sources and/or granularity levels, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this…

Novel approach for robust domain generalization in health studies.

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

We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones whe…

FRESH combines patient-level and aggregate-level data for better clinical decision making.

New method learns population dynamics from snapshots, outperforming existing models.

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and …

The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challe…

A new model synthesizes population with fewer structural and sampling zeros.

Model predicts urban population using mobile data traffic.

In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks. For example, brain connectivity networks in neuroscience consist of measures of interaction between brain regions that have been aligned to a common template. We consider the …

In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here we develop an inference method that is resilient to sampling biases and is able to …

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

AICov integrates population covariates for better COVID-19 forecasting.

GenSDR tackles SDR by leveraging generative models to fully recover lower-dimensional structures.

Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, a…

CRISP predicts individual-level COVID-19 risk based on contact data.

New method detects change-points in population genetics.

Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, …

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha…

Proposes $φ$-balancing for more balanced expert utilization in MoE models.

Improved online prediction with guaranteed coverage.

MFM integrates multiple evolving populations using Wasserstein manifold flows.

Method tackles missing covariates in large-scale datasets.

PyCFRL helps ensure fair reinforcement learning policies from offline data.

This work learns models for population dynamics using variational methods and higher-order quadrature.

The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.

o1Neuro neural network approximates complex functions and converges quickly.

Integrates neural encoders into GLMMs for multimodal data analysis.

This paper analyzes EM algorithm for softmax mixture models in high dimensions.

New algorithm balances personalization and statistical validity in MRTs.

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the breast, when tested on the screening population. We attribute the high …

Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for ho…

We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…

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

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