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.
Paper proposes faster adaptation to distribution shifts in online settings.
New method corrects seasonal Arctic sea ice predictions with probabilistic models.
Paper proposes an algorithm to optimize CVaR using retrospective approximation and importance sampling.
Retrospective and prospective analysis of Diebold-Yilmaz connectedness research.
The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.
We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) p…
We study the problem of learning a good search policy for combinatorial search spaces. We propose retrospective imitation learning, which, after initial training by an expert, improves itself by learning from \textit{retrospective inspections} of its own roll-outs. That is, when the policy eventually reaches a feasible…
The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks. This paper considers the problem…
Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care data…
Study uses healthcare claims data to identify Covid-19 risk factors without prior selection.
Estimates counterfactual outcomes linking observed and unobserved data.
New approach uses 'forward-looking' counterfactuals for treatment choice.
Improved online prediction with guaranteed coverage.
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
Study benchmarks methods for learning non-Cartesian k-space trajectories and reconstruction.
Revisits life insurance surplus models with new technical bases.
Improved stock selection through predictive fundamentals and uncertainty estimates.
Dynamic promotion optimization for e-commerce platforms within financial constraints.
Two-dimensional transition rates improve life insurance reserve calculations.
Users form information trails as they browse the web, checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. First-order and higher-order Markov chains have been widely used methods to study such se…
Novel approach uses quasi-conformal geometry for OSA classification from cephalometry.
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods fo…
On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported d…
Develops a new model-free approach to portfolio theory using rough paths.
Results on -dimensional topological planes are scattered in the literature. It is the aim of the present paper to give a survey of these geometries, in particular of information obtained after the appearance of the treatise Compact Projective Planes or not included in this book. For some theorems new proofs are give…
A new approach RA improves stochastic optimization by executing multiple steps between subsample updates.
Study analyzes portfolio performance of crypto and traditional assets.
New estimator improves policy evaluation in resource allocation RCTs.
We present an avatar of the Euler obstruction to foliated structures on certain non-metric surfaces. This adumbrates (at least for the simplest 2D-configurations) that the standard mechanism---to the effect that the devil of algebra sometimes barricades the existence of angelic geometric structures (obstruction theory …
Study improves feature acquisition for static settings in AFAPE.
A method estimates causal parameters using a latent variable recovery.
This article provides a new representation for pricing adjustments in derivatives.
Confounding bias, missing data, and selection bias are three common obstacles to valid causal inference in the data sciences. Covariate adjustment is the most pervasive technique for recovering casual effects from confounding bias. In this paper, we introduce a covariate adjustment formulation for controlling confoundi…
Taking advantage of the recent litterature on exact simulation algorithms (Beskos, Papaspiliopoulos and Roberts) and unbiased estimation of the expectation of certain fonctional integrals (Wagner, Beskos et al. and Fearnhead et al.), we apply an exact simulation based technique for pricing continuous arithmetic average…
Develops framework for estimating and improving DTRs with time-varying IV in the presence of unmeasured confounding.
The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariate adjustment sets from an assumed causal graphical model are well known. Restricting attention to causal linear models, a recent article der…
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework …
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…
Investigates adjustments on Lie group crossed modules for gauge theory.
We describe principal 3-bundles with adjusted connections using Lie algebras and groupoids.
New theory connects non-abelian bundle gerbes to abelian ones.
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next cha…
This article attempts to place the emergence of probabilistic numerics as a mathematical-statistical research field within its historical context and to explore how its gradual development can be related both to applications and to a modern formal treatment. We highlight in particular the parallel contributions of Sul'…
Efficient adjustment sets found for cost-minimized causal estimations.
The paper provides PAC bounds for estimating causal effects using covariate adjustment with a valid set.
Study optimal adjustment sets for causal policies with hidden variables.
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method does not rely on the availability of gold-standard labels, but rather uses noisy labels, called anchors, for learning. We provide a likelih…
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP and then solve it often fail because the model that best fits the data may not be well suited for planning. We introduce a new optimization objecti…