arXiv research

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

TMLE improves IPM estimation for ecological population dynamics.

The paper analyzes how data augmentation affects the test error in regression models.

Develops a weighting framework to generalize ITRs from source to target populations.

Study recovers Riemannian quantities from noisy data densities.

Estimates causal effect of managed care plans on NYC Medicaid spending.

Methods for prediction and tolerance intervals in non-normal models.

Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature. Yet, the very nature of this quantity is barely understood. Borrowing ideas from the orthogonal polynomial literature, we in…

Though machine learning algorithms excel at minimizing the average loss over a population, this might lead to large discrepancies between the losses across groups within the population. To capture this inequality, we introduce and study a notion we call maximum weighted loss discrepancy (MWLD), the maximum (weighted) d…

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

Estimates population size using capture-recapture designs with binary indicators.

Fluctuation scaling is observed phenomenon from complex networks through finance to ecology. It means that the variance and the mean of a specific quantity are related as $\ev{σ^2|n}\propto \ev{n|A}^{2α}$ with $1/2\geq α\geq 1$ when a parameter $A$ (usually the system size) is varied. $A$ can be the strength of the nod…

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

We introduce a system of kinetic equations describing an exchange market consisting of two populations of agents (dealers and speculators) expressing the same preferences for two goods, but applying different strategies in their exchanges. We describe the trading of the goods by means of some fundamental rules in price…

Information theory provides principled ways to analyze different inference and learning problems such as hypothesis testing, clustering, dimensionality reduction, classification, among others. However, the use of information theoretic quantities as test statistics, that is, as quantities obtained from empirical data, p…

We derive optimal statistical and computational complexity bounds for exp-concave stochastic minimization in terms of the effective dimension. For common eigendecay patterns of the population covariance matrix, this quantity is significantly smaller than the ambient dimension. Our results reveal interesting connections…

Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning. We often correct for biased sampling of summary statistics with matching methods and importance weighting. In this paper, we study nearest neighbor matching (NNM), which makes estimates of populati…

A new model for heterogeneous populations optimizes consumption and investment over short horizons.

Framework identifies population quantities from MNAR feedback using weak shadow variables from pretrained models.

The covariance matrix of a $p$-dimensional random variable is a fundamental quantity in data analysis. Given $n$ i.i.d. observations, it is typically estimated by the sample covariance matrix, at a computational cost of $O(np^{2})$ operations. When $n,p$ are large, this computation may be prohibitively slow. Moreover, …

We show that an economic system populated by multiple agents generates an equilibrium distribution in the form of multiple scaling laws of conditional PDFs, which are sufficient for characterizing the probability distribution. The existence of the double scaling law is demonstrated empirically for the sales and the lab…

In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the …

ENN method uses expectile regression for genetic data analysis of complex diseases.

We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood function. We derive the population quantity that is the target of this estimator and study the properties of confidence intervals (CIs) constructed from asymptotic n…

USP test improves on Pearson's chi-squared and $G$-test for independence.

The paper develops a method for forecasting power consumption at various levels of aggregation.

One-pass SGD dynamics in overparameterized quadratic networks show slow escape from poor solutions.

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be per…

Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high frequencies during the training. The F-Principle has been very use…

The paper proposes a method to improve forecast combination accuracy using portfolio theory.

Multiple cause-of-death data provides a valuable source of information that can be used to enhance health standards by predicting health related trajectories in societies with large populations. These data are often available in large quantities across U.S. states and require Big Data techniques to uncover complex hidd…

This paper tightens the law of the iterated logarithm for empirical KL_inf, applicable to unbounded data.

Study finds conserved quantities for two types of curves on conformal sphere.

Ecker's and Huisken's quantities agree for ancient mean curvature flows.

Improved multiclass classification with class-weighted nearest neighbors.

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

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

NeuPL learns diverse policies in strategy games efficiently.

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

New framework for online control in evolving populations.

Develops robust learning methods for datasets with sub-populations.

Decision support tools that rely on supervised learning require large amounts of expert annotations. Using past radiological reports obtained from hospital archiving systems has many advantages as training data above manual single-class labels: they are expert annotations available in large quantities, covering a popul…

Estimates Mozambique's population using remote sensing and microcensus data.

Estimates true Sharpe ratio of selected assets with various methods.

PBI inference may not be calibrated if predictive model is inaccurate.

Paper develops a two-population model to assess longevity basis risk.

A clustering method for multivariate populations with similar dependence structures.

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…