This week-end (🥳) I will attend the 39th Annual Meeting of the Canadian Econometrics Study Group (CESG), in Toronto, Ontario on October 25-27, 2024. I will give a talk on calibration, based on recent work, with Agathe Fernandes Machado, Ewen Gallic, Emmanuel Flachaire and François Hu. Slides are available online.
Our paper Post-Calibration Techniques: Balancing Calibration and Score Distribution Alignment, will be presented at the NeurIPS workshop on Bayesian Decision-making and Uncertainty, that will take place in Vancouver, mid December. Agathe will be there to present it…
Je serai pour les 6 prochains jours au Centre Culturel International de Cerisy, pour un colloque sur “l’assurance face à ses ruptures“. Au programme (13/9) histoire de l’assurance, (14/9) la financiarisation, (15/9) les données massives, (16/9) le changement climatique et (17/9) le rôle de l’État. Je ferais une intervention dimanche matin, sur le thème Certitudes collective et incertitudes individuelles, les données massives changent-elles la donne ?
Dans cette intervention, nous commencerons par un détour historique et sociologique en revenant sur les travaux d’Émile Durkheim et Max Weber. Ces chercheurs ont montré que bien que les actions individuelles puissent être imprévisibles, les comportements collectifs suivent des schémas réguliers. En agrégeant les actions individuelles, nous observons des régularités au sein de groupes plus larges, une idée illustrée par la prédictibilité du suicide dans le contexte des données massives. Ensuite, nous aborderons les réflexions de Herbert Simon et sa théorie de la rationalité limitée, suggérant que malgré les limites cognitives des individus, les rendant individuellement imprévisibles, des modèles prévisibles émergent dans la prise de décision globale. Nous explorerons ensuite des questions épistémologiques en discutant l’interprétation fréquentiste des probabilités, laquelle requiert la répétition pour être quantifiée, et la difficulté d’associer une probabilité à un événement unique. La réponse bayésienne, qui interprète les probabilités comme des croyances ou des scores, complique la relation avec l’équité actuarielle. Nous questionnerons la calibration des modèles et l’interprétation des scores à travers des exemples simples, tels que “avoir 70% de chances qu’une opération militaire réussisse” ou “avoir 70% de chances de pluie entre 14 et 15 heures”. Enfin, nous conclurons en prenant en compte le caractère temporel de la prévision: prévoir un accident 15 minutes avant qu’il ne survienne n’est pas équivalent à le prévoir un an à l’avance.
Les slides sont en ligne. Sinon Le Monde, quotidien français, publiait un portrait des colloques (organisés depuis 1952) cet été, il y a un mois,
Next week, I will be at the European Actuarial Journal Conference, at the Lisbon School of Economics and Business, EAJ’24.
I will give a talk on calibration of actuarial models, based on our recent paper with Agathe Fernandes Machado, Emmanuel Flachaire, Ewen Gallic and François Hu, mainly “Probabilistic Scores of Classifiers, Calibration is not Enough” (as well as recent work on recalibration). Slides are available.
In binary classification tasks, accurate representation of probabilistic predictions is essential for various real-world applications such as predicting payment defaults or assessing medical risks. The model must then be well-calibrated to ensure alignment between predicted probabilities and actual outcomes. However, when score heterogeneity deviates from the underlying data probability distribution, traditional calibration metrics lose reliability, failing to align score distribution with actual probabilities. In this study, we highlight approaches that prioritize optimizing the alignment between predicted scores and true probability distributions over minimizing traditional performance or calibration metrics. When employing tree-based models such as Random Forest and XGBoost, our analysis emphasizes the flexibility these models offer in tuning hyperparameters to minimize the Kullback-Leibler (KL) divergence between predicted and true distributions. Through extensive empirical analysis across 10 UCI datasets and simulations, we demonstrate that optimizing tree-based models based on KL divergence yields superior alignment between predicted scores and actual probabilities without significant performance loss. In real-world scenarios, the reference probability is determined a priori as a Beta distribution estimated through maximum likelihood. Conversely, minimizing traditional calibration metrics may lead to suboptimal results, characterized by notable performance declines and inferior KL values. Our findings reveal limitations in traditional calibration metrics, which could undermine the reliability of predictive models for critical decision-making.
This week, Fallou Niakh will be in Vienna, at the Workshop “Climate Change and Insurance“, to present our recent joint work, witn Philipp Ratz and Caroline Hillairet
On Wednesday morning, I will be chairing our session “Discrimination-free Insurance Pricing” at the Insurance: Mathematics & Insurance Conference, in Chicago. With Olivier Côté, Lydia Gabric and Hong Beng Lim, we will be four speaker, just before lunch time. My talk will be a mix of recent work on quantifying and mitigating discrimination in scores (in insurance). Slides are available online.
Monday, I will be giving a short course at the workshop on Decentralized Insurance and Risk Sharing (SAC 161), in Chicago
My slides are available online.
In this course, we will get back to mathematical properties of risk sharing on networks, with reciprocal contrats. We will discuss conditions about stochastic dominance, proving that policyholers might have interest in sharing risks with “friends”.
Then, we will try to adress fairness issues, for such risk sharing mechanisms. If fairness has been recently intensively studied, either through group or individual fairness, there are yet not much litterature about fairness on networks. It is important to adress those issues, since perceived discrimination is usually associated with networks. We will see why the topology of the network is important, both to design peer-to-peer schemes to share risks, but also to see if perceived discrimination is associated with global disparate treatement.
I will be in Chicago next week, invited by 冯润桓 (Runhuan Feng). On Monday, with Jan Dhaene and Marco Mirabella, we will give some short courses “on decentralized insurance and risk sharing“.
Then, we will attend the IME Congress 2024 (that will be held between July 9th and 11th).
After defending his PhD last week, Samuel just arrived in 横浜市, at the International Joint Conference on Neural Networks (IJCNN’24), that will take place at the IEEE World Congress on Computational Intelligence (WCCI).
He will present our recent work on Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled Autoencoder for Mixed Tabular Datasets,
The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image 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. Autoencoders are widely employed for learning and constructing a new representation of a dataset, particularly for dimensionality reduction. They are also often used for generative model learning, as seen in variational autoencoders. When dealing with mixed tabular data, qualitative variables are often encoded using a one-hot encoder with a standard loss function (MSE or Cross Entropy). In this paper, we analyze the drawbacks of this approach, especially when categorical variables are imbalanced. We propose a novel metric to balance learning: a Multi-Supervised Balanced MSE. This approach reduces the reconstruction error by balancing the influence of variables. Finally, we empirically demonstrate that this new metric, compared to the standard MSE: i) outperforms when the dataset is imbalanced, especially when the learning process is insufficient, and ii) provides similar results in the opposite case.
This week, I will attend the Insurance Data Science conference in Sweeden. It has been a while… I was a keynote speaker at the one in London, ten years ago (to give a talk I still have feedbacks about – Getting into Bayesian Wizardry… (with the eyes of a muggle actuary) – by that time, the conference was “R in Insurance”), and then, we organized the one in Paris, back in 2017. Then we had the online events, but it was… different.
This time, I will get back to our recent paper A Sequentially Fair Mechanism for Multiple Sensitive Attributes, with François Hu and Philipp Ratz, and the equipy package, wrote with Agathe Fernandes-Machado and Suzie Grondin. The slides are available online.
The next two weeks, in will be in (Northern) Europe, with a first stop in Brussels (to visit colleagues), then in Leuven (I will give a talk on Monday at KU Leuven), then in København (I will give a talk on Friday at Københavns Universitet), and finally in Stockholm (at Stockholm University, for the Insurance Data Science conference).
In the Fall, I will be in Europe, with Lisbon (European Actuarial Journal conference), in France (Cerisy Colloques) and in Warsaw in Poland. In Poland, I will give a two day cours on Insurance, Biases, Discrimination and Fairness…
More to come soon…
Jeudi, je vais participer, à distance, au 23e congrès de l’Institut des Actuaires, en France, avec Florence Picard et Laurence Barry.
Notre exposé a pour titre “assurabilité : vers de nouveaux partages de risques?”. Je parlerais un peu des catastrophes naturelles… et du risque de sécheresse, ou plutôt du risque “RGA“.
Continue reading Assurabilité, vers de nouveaux partages de risque, Congrès des Actuaires
Tomorrow, Ewen will attend the 58th Annual Meetings of the Canadian Economics Association, to present our recent work on calibration…. (more soon)
This Monday, a Workshop on Trustworthy AI will be held May 27, 2024 in Montreal.
We will be there with Agathe and Olivier, to chat with people who might have some interest
Here are our posters. I wil talk about discrimination and insurance
Agathe will explain why callibration of scores is important,
and finally, Olivier will talk about building (causal) graphs for fairness
The next iteration of the Conference on New Developments in Probability (CNDP) will take place from September 26-28, 2024 [Thursday morning-Saturday noon] in Montreal, Quebec, Canada. The event will be hosted by Women in Probability and the Centre de Recherches Mathématiques (CRM)…
