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.
Study on insurance risk management and sustainable development.
Model predicts active and passive cosponsorship motivations in Congress.
The number of Italian firms in function of the number of workers is well approximated by an inverse power law up to 15 workers but shows a clear downward deflection beyond this point, both when using old pre-1999 data and when using recent (2014) data. This phenomenon could be associated with employent protection legis…
Machine learning is becoming an ever present part in our lives as many decisions, e.g. to lend a credit, are no longer made by humans but by machine learning algorithms. However those decisions are often unfair and discriminating individuals belonging to protected groups based on race or gender. With the recent General…
Probabilistic methods for classifying text form a rich tradition in machine learning and natural language processing. For many important problems, however, class prediction is uninteresting because the class is known, and instead the focus shifts to estimating latent quantities related to the text, such as affect or id…
One important effect of price shocks in the United States has been increased political attention paid to the structure and performance of oil and natural gas markets, along with some governmental support for energy conservation. This paper describes how price changes helped lead the emergence of a political agenda acco…
The creation of the Renewable Energy Law (Law 1715 of 2014) promotes the introduction of large-scale renewable energy generation in the Colombian electricity market. The new legislation aims to diversify the country's generation matrix, mainly composed of hydro and fuel-based generation, with a share of 66% and 34% res…
Synthetic interventions extend SC method to multiple treatments.
We develop a model of issue-specific voting behavior. This model can be used to explore lawmakers' personal voting patterns of voting by issue area, providing an exploratory window into how the language of the law is correlated with political support. We derive approximate posterior inference algorithms based on variat…
Bayesian Federated Inference improves survival model analysis without merging data.
Paper shows incorrectness of approximate unlearning definitions and challenges exact unlearning verification.
We present a framework on how to hedge the interest rate sensitivity of liabilities discounted by an extrapolated yield curve. The framework is based on functional analysis in that we consider the extrapolated yield curve as a functional of an observed yield curve and use its Gâteaux variation to understand the sensiti…
The aim of this paper is to introduce an insurance model allowing reinsurance and dividend payment. Our model deals with several homogeneous contracts and takes into account the legislation regarding the provisions to be justified by the insurance companies. This translates into some restriction on the (maximal) number…
Research aims to make fact-checking models more transparent.
This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is fou…
Derives a Matern Gaussian process on hypergraphs for regression and embedding.
Linear filtration helps delete training data from models.
Examines international taxation's impact on Georgian businesses.
Federated Learning solves privacy and data distribution challenges in machine learning.
Modeling preference rankings with salient features to explain irrational choices.
Paper proposes a fast method for approximate data deletion in generative models.
Bayesian inference forgetting framework removes influence of single data points.
Develops a new tensor model for clustering with degree correction.
Paper proposes first unlearning algorithm for MCMC models.
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle p…
Enhances content moderation with culturally-aware models.
The economic equities maximization criterion (MFPE) leads to the choice of financial portfolio, which maximizes the ratio of the expected value of the insurance company on the capital. This criterion is presented in the framework of a non-life insurance company and is applied within the framework of the French legislat…
Proposes a general deep neural network method for digital watermarking.
This paper analyzes FL privacy risks and defensive strategies.
The aim of this paper is to introduce a synthetic ALM model that catches the main specificity of life insurance contracts. First, it keeps track of both market and book values to apply the regulatory profit sharing rule. Second, it introduces a determination of the crediting rate to policyholders that is close to the p…
This paper tests LLMs in finance to assess ethical behavior.
Study data biases to predict algorithmic discrimination, developing a Data Bias Profile.
Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills …
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and t…
Examines AI regulation in finance, highlighting risks and gaps in current laws.
FELICIA uses a centralized adversary to improve synthetic medical image generation.
DVWU framework improves model performance by considering data value heterogeneity.
r-STSF improves TSC accuracy and interpretability.
A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social m…
Adaptive uncertainty quantification improves black-box model predictions in generative AI.
This guide simplifies explainable deep learning for beginners.
A dataset for detecting online hate speech from YouTube and Reddit comments.
Georgia needs a new budget code to manage fiscal policies effectively.
Machine learning algorithms often contain many hyperparameters (HPs) whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these HP configurations and their complex interactions, it is common to use optimization techniques to find settings th…
New framework for contesting algorithmic decisions, not just explaining them.
Study examines how mergers and acquisitions affect Indian banks' financial performance and capital structure.
The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. The research focus has mostly been on improving the accuracy and efficiency of classifiers, while their interpretability has been somewhat neglected. Classifier interpretabil…
Study evaluates UK CDC schemes, finding intergenerational cross-subsidies in flat-accrual schemes and dynamic-accrual schemes can reduce but not eliminate them.