WildWood: a new Random Forest algorithm

We introduce WildWood (WW), a new ensemble algorithm for supervised learning of Random Forest (RF) type. While standard RF algorithms use bootstrap out-of-bag samples to compute out-of-bag scores, WW uses these samples to produce improved predictions given by an aggregation of the predictions of all possible subtrees of each fully grown tree in the forest. This is achieved by aggregation with exponential weights computed over out-of-bag samples, that are computed exactly and very efficiently thanks to an algorithm called context tree weighting. This improvement, combined with a histogram strategy to accelerate split finding, makes WW fast and competitive compared with other well-established ensemble methods, such as standard RF and extreme gradient boosting algorithms.

About contrastive unsupervised representation learning for classification and its convergence

Contrastive representation learning has been recently proved to be very efficient for self-supervised training. These methods have been successfully used to train encoders which perform comparably to supervised training on downstream classification tasks. A few works have started to build a theoretical framework around contrastive learning in which guarantees for its performance can be proven. We provide extensions of these results to training with multiple negative samples and for multiway classification. Furthermore, we provide convergence guarantees for the minimization of the contrastive training error with gradient descent of an overparametrized deep neural encoder, and provide some numerical experiments that complement our theoretical findings

ZiMM: a deep learning model for long term adverse events with non-clinical claims data

This paper considers the problem of modeling long-term adverse events following prostatic surgery performed on patients with urination problems, using the French national health insurance database (SNIIRAM), which is a non-clinical claims database built around healthcare reimbursements of more than 65 million people. This makes the problem particularly challenging compared to what could be done using clinical hospital data, albeit a much smaller sample, while we exploit here the claims of almost all French citizens diagnosed with prostatic problems (with between 1.5 and 5 years of history). We introduce a new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) to capture such long-term adverse events, and we build a deep-learning architecture on top of it to deal with the complex, highly heterogeneous and sparse patterns observable in such a large claims database. This architecture combines several ingredients: embedding layers for drugs, medical procedures, and diagnosis codes; embeddings aggregation through a self-attention mechanism; recurrent layers to encode the health pathways of patients before their surgery and a final decoder layer which outputs the ZiMM's parameters.