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.
ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.
Satellite imagery improves house price prediction models.
Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.
This paper presents an assessment of global economic energy potentials for all major natural energy resources. This work is based on both an extensive literature review and calculations using natural resource assessment data. Economic potentials are presented in the form of cost-supply curves, in terms of energy flows …
Framework for assessing fairness across similar predictive models.
Paper introduces new risk measures for Kelly criterion.
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
Robust method estimates self-similarity for mammogram images, improving cancer detection.
Solvency II Directive 2009/138/EC requires an insurance and reinsurance undertakings assessment of a Solvency Capital Requirement by means of the so-called "Standard Formula" or by means of partial or full internal models. Focusing on the first approach, the bottom-up aggregation formula proposed by the regulator permi…
We show how risk measures originally defined in a model free framework in terms of acceptance sets and reference assets imply a meaningful underlying probability structure. Hereafter we construct a maximal domain of definition of the risk measure respecting the underlying ambiguity profile. We particularly emphasise li…
E-scores assess LLM outputs for correctness, addressing p-hacking issues.
Improves pre-trial risk assessments by making them safer without changing existing rules.
The ROC curve is widely used to assess the quality of prediction/classification/ranking algorithms, and its properties have been extensively studied. The precision-recall (PR) curve has become the de facto replacement for the ROC curve in the presence of imbalance, namely where one class is far more likely than the oth…
The paper analyzes the risk of investing in a basket of 27 cryptocurrencies using statistical distributions.
Proposes CCE to assess point-wise reliability of neural network predictions.
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…
Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification methods tailored for extreme events, such as thresholded and weighted …
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification f…
The study simplifies assessing overlap in logistic regression models using empirical likelihood.
AI systems need reliable testing to ensure safety and trustworthiness.
New test assesses probabilistic model calibration without expensive approximations.
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates fea…
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices of risk function (e.g. VaR vs CVaR); choice of return distribution (p…
New conditional risk measures called conditional generalized quantiles defined and characterized.
Study examines dependence properties of Bayesian neural network units in finite-width networks.
Radio-frequency dosimetry is an important process in human safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-variability of subjects. However, the common procedure to develop person…
In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generaliz…
In this paper, as a first step in examining the properties of a feasible portfolio subset that is characterized by budget and risk constraints, we assess the maximum and minimum of the investment concentration using replica analysis. To do this, we apply an analytical approach of statistical mechanics. We note that the…
The paper introduces a US crime index to assess financial losses from property and cyber crimes.
Graph networks struggle with multi-task learning due to varying property loss surface curvatures.
New method evaluates visual explanations of deep models using adversarial perturbations.
The average portfolio structure of institutional investors is shown to have properties which account for transaction costs in an optimal way. This implies that financial institutions unknowingly display collective rationality, or Wisdom of the Crowd. Individual deviations from the rational benchmark are ample, which il…
One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse. In this work, we construct a novel measure of performance of a GAN by comparing geometrical properties of the underlying data manifold and …
Persistent homology enhances graph classification by capturing long-range graph properties.
Framework assesses autograders' reliability and biases.
Paper assesses how features influence classification of COVID-19 patients.
Bayesian test assesses dependence between mixed data types.
The paper introduces CoCoCat bonds for multi-region natural catastrophes, accounting for complex dependencies.
The paper examines statistical properties of IL and LVR in automated market makers.
Protein structure prediction has been a grand challenge problem in the structure biology over the last few decades. Protein quality assessment plays a very important role in protein structure prediction. In the paper, we propose a new protein quality assessment method which can predict both local and global quality of …
The paper assesses the risk of negative treatment effects using bounds and inference.
Proposes a decentralized insurance protocol for DeFi.
Study evaluates and compares traditional and causal machine learning methods for estimating direct price effects of environmental amenities.
Reply to Tetlock et al. on tail risk and probability gap.
Risk measures such as Expected Shortfall (ES) and Value-at-Risk (VaR) have been prominent in banking regulation and financial risk management. Motivated by practical considerations in the assessment and management of risks, including tractability, scenario relevance and robustness, we consider theoretical properties of…
This work introduces benchmarks for evaluating nanophotonic structures in design simulations.
The paper uses double machine learning to estimate dynamic treatment effects robustly.
This paper is a continuation of Ishitani and Kato (2015), in which we derived a continuous-time value function corresponding to an optimal execution problem with uncertain market impact as the limit of a discrete-time value function. Here, we investigate some properties of the derived value function. In particular, we …