Seminar from the PhD students of EDMH
By Louise Davy, Romain Therezien

From 12:00 to 1:30pm, room TBA.

Louise Davy (S2A) : Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale. We demonstrate how a frugal approach that retains only a fraction of the available information on pairs can achieve estimation or optimization performance comparable to that obtained by using all pairs, by leveraging survey sampling techniques. A central finding, supported by both theory and experiments, is that such sampling plans must target pairs directly rather than individual observations. In particular, for pairwise losses between high-dimensional vectors such as embeddings in vision or graph learning, assigning higher inclusion probabilities to informative pairs using suitable auxiliary information yields performance close to full pairwise evaluation, providing a principled and theoretically grounded trade-off between accuracy and computational cost.

Romain Therezien (S2A) : On Pairwise Quantile Regression - Statistical Guarantees and Applications

Quantile regression is a powerful tool for analyzing the conditional distribution of a response variable beyond its mean, especially in the presence of high variability. We extend the methodology to pairwise settings, where the response is a similarity score between two observations and the predictors are their associated covariates. We introduce a pairwise quantile regression framework based on the pinball loss and establish theoretical guarantees, including generalization bounds and fast learning rates using concentration results for U-processes. Simulations confirm the validity of the approach, while an application to facial recognition demonstrates its usefulness for understanding and characterizing similarity-scoring errors in biometric systems. ng, hiking, and photography.