arXiv research

This paper develops a framework for predicting patient workload across multiple VA facilities.

VA-LUCB identifies best arm with variance constraint, achieving optimal sample complexity.

Variable annuities (VA) are popular insurance products. VAs provides the insured with a guaranteed accumulation rate on their premium at maturity. In addition, the insured may receive extra benefit if returns of underlying funds are high enough. Here we consider a special case of VA with high-water mark feature and Gua…

Personalized deep learning reduces inappropriate shocks in VA detection.

Improved financial VA intent classification accuracy.

New tests detect asphericity in complex pairs, simplifying previous proofs.

Study predicts lens performance using neural networks.

This paper stems from the observation (arising from work of T. Delzant) that "most" Kähler groups virtually algebraically fiber, i.e. admit a finite index subgroup that maps onto $\Bbb{Z}$ with finitely generated kernel. For the remaining ones, the Albanese dimension of all finite index subgroups is at most one, i.e. t…

Study analyzes fees linked to VIX index in annuity contracts.

Investigates optimal withdrawal strategies in VA contracts with tax and ratchet mechanisms.

LDP speeds up causal discovery by partitioning, improving VAS recall and runtime.

Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.…

Paper develops a method for valid inference using language model predictions from verbal autopsy narratives.

The paper addresses fairness in facility location by considering local population densities.

Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variationa…

Variable Annuity (VA) products expose insurance companies to considerable risk because of the guarantees they provide to buyers of these products. Managing and hedging these risks requires insurers to find the value of key risk metrics for a large portfolio of VA products. In practice, many companies rely on nested Mon…

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…

Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.

Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to valu…

The guaranteed minimum withdrawal benefit (GMWB) rider, as an add on to a variable annuity (VA), guarantees the return of premiums in the form of peri- odic withdrawals while allowing policyholders to participate fully in any market gains. GMWB riders represent an embedded option on the account value with a fee structu…

The purpose of this article is twofold. First, we motivate the need for a new type of stand-alone retirement income insurance product that would help individuals protect against personal longevity risk and possible "retirement ruin" in an economically efficient manner. We label this product a ruin-contingent life annui…

Proposes a new method to find overconfident predictions in classifiers.

MPNN improves on UniFL approximation with provable guarantees.

Deployable probabilistic programming for Go and other languages.

VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.

Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.

Study examines how industrial emissions evolve over time in response to various factors.

SimTensor is a multi-platform, open-source software for generating artificial tensor data (either with CP/PARAFAC or Tucker structure) for reproducible research on tensor factorization algorithms. SimTensor is a stand-alone application based on MATALB. It provides a wide range of facilities for generating tensor data w…

Based on the concept of self-decomposable random variables we discuss the application of a model for a pair of dependent Poisson processes to energy facilities. Due to the resulting structure of the jump events we can see the self-decomposability as a form of cointegration among jumps. In the context of energy faciliti…

We construct a binomial model for a guaranteed minimum withdrawal benefit (GMWB) rider to a variable annuity (VA) under optimal policyholder behaviour. The binomial model results in explicitly formulated perfect hedging strategies funded using only periodic fee income. We consider the separate perspectives of the insur…

Paper assesses GMMB in VAs using FST for accurate net liability calculations.

Paper tackles multi-task learning for emotion recognition and generation using Aff-Wild dataset.

Trading floors need to be twice as deep as electronic markets to compete.

New tools analyze data selection methods, reducing information losses.

A method for quickly determining deployment schedules that meet a given fuel cycle demand is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of depl…

Develops a Bayesian model to predict business revenue and demand.

Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.

Local discovery method uncovers direct unfairness in complex systems.

Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of…

We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which as…

The paper uses GPR to speed up pricing of GMWB VA with stochastic vol and rate.

The paper optimizes daily storage trading of electricity using dynamic spread densities.

This paper offers a financial economic perspective on the optimal time (and age) at which the owner of a Variable Annuity (VA) policy with a Guaranteed Living Withdrawal Benefit (GLWB) rider should initiate guaranteed lifetime income payments. We abstract from utility, bequest and consumption preference issues by treat…

Apricot selects subsets from large data sets efficiently using submodular optimization.

The paper tackles Nash-regret minimization in congestion games with bandit feedback.

The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimization of functions. However, most proposed approaches only allow the exploration of the parameter space to occur sequentially. Often, it is…

Study on energy storage's impact on electricity prices and profitability.