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.
The Prescriptive Canvas improves business outcomes by directly prescribing actions based on predictions.
CPR models complex decision processes by breaking them into context-specific policies, improving interpretability and accuracy.
Paper integrates predictive and prescriptive tasks using bilevel optimization.
Deep learning predicts drug prescriptions across global health records.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
A model learns symptom-drug relations for PD patients.
The study compares Euclidean and cosine distances in medical drug prescription prediction.
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple pr…
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
Achilles predicts Gold vs USD with a profitable trading bot.
PNNs improve treatment outcomes in TAVR and liver trauma.
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a robustified nonlinear model, and (ii) adopting a randomized prescriptiv…
A new neural network model for optimal treatment assignment.
VR game data for P300 BCI with raccoon vs demon stimuli.
PNNs improve personalized healthcare policies using mixed integer programming.
Paper proposes a method to quantify and explain machine learning uncertainty in predictive process monitoring.
Improves uncertainty estimation and OOD detection in neural networks.
We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual in…
Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o…
Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree…
We augment linear Support Vector Machine (SVM) classifiers by adding three important features: (i) we introduce a regularization constraint to induce a sparse classifier; (ii) we devise a method that partitions the positive class into clusters and selects a sparse SVM classifier for each cluster; and (iii) we develop a…
Local classification of surfaces and hypersurfaces with radial mean curvature.
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood …
Paper solves curvature prescription problem on surfaces with boundary.
P.Lecomte has proposed to take into account the covariant derivatives used to build ordering prescriptions for the naturality of transformation properties and has conjectured that there exists an natural ordering prescription for differential operators of any orders between density bundles which in addition is invarian…
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction task, several formulations exist, depending on how one chooses the time of prediction and the prediction horizon. In this paper, we show how…
Boosting strategies for merging vs. ensembling studies analyzed.
New approach for estimating individual treatment effects in low compliance settings.
Prescribing, by conformal transformation, the kth-elementary symmetric polynomial of the Schouten tensor to be constant is a generalisation of the Yamabe problem. On compact Riemannian n-manifolds we show that, for k between and including 3 and n, this prescription equation is an Euler-Lagrange equation of some act…
Study proves curvature prescription on spheres for k ≥ n/2.
RS-NN predicts belief functions for classification, improving accuracy and uncertainty estimation.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
In this paper we give a new proof of a theorem by Alexandrov on the Gauss curvature prescription of Euclidean convex sets. This proof is based on the duality theory of convex sets and on optimal mass transport. A noteworthy property of this proof is that it does not rely neither on the theory of convex polyhedra nor on…
Method learns optimal treatment policies from observational data.
Conformal Bayes under label shift: post-hoc calibration vs. in-training adaptation
Study improves dynamic PT fleet optimization under noisy demand predictions.
Two problems concerning asymptotically hyperbolic manifolds with an inner boundary are studied. First, we study scalar curvature presciption with either Dirichlet or mean curvature prescription interior boundary condition. Then we apply those results to the Lichnerowicz equation with (future or past) apparent horizon i…
In this paper, we study conformal invariants that arise from nodal sets and negative eigenvalues of conformally covariant operators; more specifically, the GJMS operators, which include the Yamabe and Paneitz operators. We give several applications to curvature prescription problems. We establish a version in conformal…
For any compact manifold of dimension n>=5, we prescribe the volume and any finite part of the spectrum of the Hodge Laplacian acting on diffential forms of degree 1<p<n-1 (exept for p=n/2 if n is even), within a given conformal class. When n<5 and when p=0,1,n-1,n, and p=n/2 if n is even, this simultaneous prescriptio…
New method fuses optical and SAR data to fill LAI gaps during cloudy periods.
Quantitative CT predicts ILD patterns and prognosis.
In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practic…
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
Study develops models to predict severe COVID-19 progression.
Optimizes decisions without knowing the true distribution using historical data.
We prove that, for a generic set of smooth prescription functions on a closed ambient manifold, there always exists a nontrivial, smooth, closed hypersurface of prescribed mean curvature . The solution is either an embedded minimal hypersurface with integer multiplicity, or a non-minimal almost embedded hypersur…
Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. "Epoch…
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc…