arXiv research

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

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

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

Models predict probabilities of causation from limited data.

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

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…

New algorithm balances personalization and statistical validity in MRTs.

Efficiently estimates variable importance in prediction tasks using Shapley values.

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…

Integrates neural encoders into GLMMs for multimodal data analysis.

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

o1Neuro neural network approximates complex functions and converges quickly.

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…

Bayesian approach clusters survival data for better risk prediction.

dLDS models neural dynamics as sparse combinations of simpler components.

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

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

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…

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

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…

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.

Neural networks can learn relationships that traditional models cannot.

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

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 …

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…

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

Proves EM algorithm guarantees for hierarchical imitation learning.

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…

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…

Optimizes subgroup selection in clinical trials.

SaML guides ML models to avoid survey biases.

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 …

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

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

Proposes a deep learning method for effective data representation.

PyCFRL helps ensure fair reinforcement learning policies from offline data.

Generative models improve causal effect estimation from observational data.

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

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

A new method for efficient causal structure learning at scale.

Paper proposes a shape-constrained approach to distributionally robust learning.

The paper studies how noisy labels impact decision-making in machine learning.

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient hea…

LACD uses unlabeled data to improve conditional diffusion models.

Study creates a multimodal learning framework for CVD risk prediction.

Method estimates heterogeneous causal effects on networks using orthogonal learning.