arXiv research

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

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

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, …

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

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…

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

New algorithm balances personalization and statistical validity in MRTs.

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…

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 …

Efficiently estimates variable importance in prediction tasks using Shapley values.

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

Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statisti…

Models predict probabilities of causation from limited data.

Multi-party machine learning leaks global dataset properties even with black-box access.

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…

TILT improves target domain performance by penalizing an auxiliary component on unlabeled target inputs.

A new method combines synthetic data analysis and DP generation to produce accurate uncertainty estimates.

o1Neuro neural network approximates complex functions and converges quickly.

The study analyzes the performance of statistical estimators under stability and computational efficiency.

Integrates neural encoders into GLMMs for multimodal data analysis.

The paper explores various forms of calibration scores and their implications for fairness.

Optimizes subgroup selection in clinical trials.

dLDS models neural dynamics as sparse combinations of simpler components.

Paper identifies unobserved variables from observable data.

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…

In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients us…

New method aligns brain data across individuals for better brain decoding.

Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom eva…

The paper introduces Shapley curves for measuring variable importance in nonparametric settings.

New framework shows diverse training data improves subgroup and overall performance.

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

Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is…

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the …

G-Net uses deep learning for complex counterfactual outcome prediction.

Neural networks can learn relationships that traditional models cannot.

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

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 …

Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned …

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

We investigate the relationship between the structure of a discrete graphical model and the support of the inverse of a generalized covariance matrix. We show that for certain graph structures, the support of the inverse covariance matrix of indicator variables on the vertices of a graph reflects the conditional indepe…

Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large. Most investigators estimate such personalized treatment effects by regressing the outcome of a randomized clinical trial (RCT) on patient covariates. The realized value of the ou…

Generative models improve causal effect estimation from observational data.

Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tw…

SaML guides ML models to avoid survey biases.

Proves EM algorithm guarantees for hierarchical imitation learning.

The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.

Develops a nonparametric graphical model for conditional independence.

Study shows fast rates for inverse reinforcement learning with linear rewards.