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.
Simulation framework assesses ROI of chronic disease adherence and policy timing.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
Smart bin monitors predict medication adherence with high accuracy.
Religious adherence reduces corporate greenwashing behavior.
Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. Thi…
The paper tackles AI advice giving by considering adherence levels and defer options.
HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.
OptiLIME improves LIME explanations by balancing stability and adherence.
Study optimal and equitable encouragement policies for treatment adherence.
Algorithm minimizes regret while adhering to unknown safety constraints.
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. T…
Proposes a Gaussian process model for constrained dynamics learning.
New approach tackles non-Markovian behavior in maternal health programs.
Paper tackles inventory management with deep learning, improving performance and adherence to constraints.
latrend simplifies longitudinal clustering for numeric measurements.
Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two …
dCMF models evolving patterns in multiway data with temporal dynamics.
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation mod…
System predicts HIV patients at risk of dropping out of care.
New testing method for robust actor-critic bandit algorithms.
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This…
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions.…
Proposes a method to generate text that adheres to logical constraints.
The study finds optimal curvature pinching in Heintze groups.
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns …
Learning an encoding of feature vectors in terms of an over-complete dictionary or a information geometric (Fisher vectors) construct is wide-spread in statistical signal processing and computer vision. In content based information retrieval using deep-learning classifiers, such encodings are learnt on the flattened la…
We present a deep learning system for testing graphics units by detecting novel visual corruptions in videos. Unlike previous work in which manual tagging was required to collect labeled training data, our weak supervision method is fully automatic and needs no human labelling. This is achieved by reproducing driver bu…
Generative model predicts menstrual cycle lengths accounting for self-tracking artifacts.
We introduce a local volatility model for the valuation of options on commodity futures by using European vanilla option prices. The corresponding calibration problem is addressed within an online framework, allowing the use of multiple price surfaces. Since uncertainty in the observation of the underlying future price…
A theory which describes the share price evolution at financial markets as a continuous-time random walk has been generalized in order to take into account the dependence of waiting times t on price returns x. A joint probability density function (pdf) which uses the concept of a Lévy stable distribution is worked out.…
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a …
Algorithm samples constrained stochastic differential equations.
New method constructs asymptotic convex hypersurfaces via equidistant hyperplanes.
This paper presents a new model for pricing financial derivatives subject to collateralization. It allows for collateral arrangements adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a uni…
skfolio optimizes portfolios using Python, integrating machine learning.
Meta-theorems validate fair regression algorithms under demographic parity constraints.
Mobile apps and machine learning improve malaria prevention and treatment.
Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion, outlier trimming, and post hoc outlier identification methods, with the forme…
Algorithm combines ESG ratings with pairs trading for sustainable investing.
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adh…
Bayesian approach for solving systems of linear PDEs with boundary conditions.
New method uses quantum annealing and VAN for better statistical mechanics calculations.
A new deep learning method for option pricing in rough volatility models.
Accounting frameworks follow stipulations of existing Accounting Theories. This exploratory research sets out to trace the evolution of accounting theories of Charge and Discharge Syndrome and the Corollary of Double Entry. Furthermore, it dives into the theories of Income Determination, garnishing it with areas of div…
The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence the…
WCPS extends CPS to handle covariate shifts, providing probabilistically calibrated predictions.
Optimized portfolio turnover strategies enhance wealth and reduce costs.
The horocyclic flow on geometrically infinite surfaces shows recurrent irregular orbits or non-minimal closures.