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Found 22 papers, 11 papers with code
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders.
From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration
The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy.
Geospatial Disparities: A Case Study on Real Estate Prices in Paris
Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models.
Measuring and Mitigating Biases in Motor Insurance Pricing
The non-life insurance sector operates within a highly competitive and tightly regulated framework, confronting a pivotal juncture in the formulation of pricing strategies.
Parametric Fairness with Statistical Guarantees
Algorithmic fairness has gained prominence due to societal and regulatory concerns about biases in Machine Learning models.
A Sequentially Fair Mechanism for Multiple Sensitive Attributes
Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness.
Fairness Explainability using Optimal Transport with Applications in Image Classification
Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes.
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory
In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates which can be used in classification and regression.
Optimal Vaccination Policy to Prevent Endemicity: A Stochastic Model
The analysis of the model's equilibria reveals a criterion for the existence of an endemic equilibrium, which depends on the rate of immunity loss and the distribution of time between booster doses.
Mitigating Discrimination in Insurance with Wasserstein Barycenters
Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation is desirable.
Fairness in Multi-Task Learning via Wasserstein Barycenters
Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data.
Data Augmentation for Imbalanced Regression
In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates.
Optimal Transport for Counterfactual Estimation: A Method for Causal Inference
Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x.
Quantifying fairness and discrimination in predictive models
The analysis of discrimination has long interested economists and lawyers.
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
This paper designs a sequential repeated game of a micro-founded society with three types of agents: individuals, insurers, and a government.
The Fairness of Machine Learning in Insurance: New Rags for an Old Man?
Since the beginning of their history, insurers have been known to use data to classify and price risks.
A Fair Pricing Model via Adversarial Learning
In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
Weighted asymmetric least squares regression with fixed-effects
The fixed-effects model estimates the regressor effects on the mean of the response, which is inadequate to summarize the variable relationships in the presence of heteroscedasticity.
Collaborative Insurance Sustainability and Network Structure
In this paper, describe and analyse such a P2P product, with some reciprocal risk sharing contracts.
Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning
Theoretically, it is shown that it implements the autocalibration concept in pure premium calculation and ensures that balance also holds on a local scale, not only at portfolio level as with existing bias-correction techniques.
Local Utility and Multivariate Risk Aversion
We revisit Machina's local utility as a tool to analyze attitudes to multivariate risks.
Reinforcement Learning in Economics and Finance
As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices.
