A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Estimates personalized treatment response curves using covariates.
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are ei…
Paper develops methods to estimate derivative of dose-response curve for continuous treatments.
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…
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…
Proposes estimators for complex dose-response curves using kernel methods.
New kernel methods estimate complex causal relationships.
Study develops efficient algorithm for probabilistic penetration response of composite plates.
ContiVAE estimates individual dose-response curves from unobserved confounders using observational data.
Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our propos…
This paper introduces a new property of estimators of the strength of statistical association, which helps characterize how well an estimator will perform in scenarios where dependencies between continuous and discrete random variables need to be rank ordered. The new property, termed the estimator response curve, is e…
Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions and of the response belonging to either class, probability distributions for the…
Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The costs of false positives and false negatives vary across application domains and receiver operating characteristic (ROC) curves provide a visu…
A short proof for curve lengths on hyperbolic surfaces.
The paper predicts responses on out-of-sample nodes using latent positions on unknown curves.
Constructs bivariate quantiles using vine copulas for multivariate analysis.
The literature of heavy tails (typically) starts with a random walk and finds mechanisms that lead to fat tails under aggregation. We follow the inverse route and show how starting with fat tails we get to thin-tails when deriving the probability distribution of the response to a random variable. We introduce a general…
Functional BART adds shape priors to Bayesian tree regression for better curve fitting.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
Kernel method estimates long-term effects from short-term data.
Single training run learns optimal VAE parameters for various β values.
Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model …
Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in …
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain…
Paper tackles temporal overfitting in wind power curve modeling.
Study finds anomalies in high-frequency S&P 500 price changes.
The vast majority of current machine learning algorithms are designed to predict single responses or a vector of responses, yet many types of response are more naturally organized as matrices or higher-order tensor objects where characteristics are shared across modes. We present a new machine learning algorithm BaTFLE…
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this paper, we propose the -IRT model, which models continuous responses and can generate a much enriched family of Item Characteristic Cur…
New algorithm speeds up IRT model fitting for large datasets.
New geometric theory explains nonuniform origami responses.
Unified framework for binary responses using AUC loss and low-rank constraint.
We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in pr…
Neural network predicts functional responses from scalar inputs.
New method estimates effects of multiple nutrients on blood glucose.
Estimates individualized treatment effects using shared RBF-net neurons.
Deployment-complete benchmarking assesses if evidence leads to consistent deployment actions.
Using the trends of estimated abilities in terms of item response theory for online testing, we can predict the success/failure status for the final examination to each student at early stages in courses. In prediction, we applied the newly developed nearest neighbor method for determining the similarity of learning sk…
GLMM trees identify subgroups with different growth patterns in longitudinal data.
Objective: Predict individual septic children's personalized physiologic responses to vasoactive titrations by training a Recurrent Neural Network (RNN) using EMR data. Materials and Methods: This study retrospectively analyzed EMR of patients admitted to a pediatric ICU from 2009 to 2017. Data included charted time se…
Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of construction details on the structural performance of installations under se…
Study uses ML to predict non-participation in ELSA COVID-19 follow-up studies.
Spatially-aware model improves earthquake hazard assessment accuracy.
The paper develops predictors for functional data on manifolds.
Efficient methods estimate concordance probability for big data.
Innovative ball bearing converts rotary to reciprocating motion.
Method bounds continuous-valued treatment effects when confounding variables are hidden.
We analyze DMs using spectral methods to design effective noise schedules.
We develop theory and computational methods to investigate particle inclusions embedded within curved lipid bilayer membranes. We consider the case of spherical lipid vesicles where inclusion particles are coupled through (i) intramembrane hydrodynamics, (ii) traction stresses with the external and trapped solvent flui…