Tootfinder

Weak convergence of Bayes estimators under general loss functions
Robin Requadt, Housen Li, Axel Munk
https://arxiv.org/abs/2510.05645 https://arxiv.org/pd…

Weak convergence of Bayes estimators under general loss functions
We investigate the asymptotic behavior of parametric Bayes estimators under a broad class of loss functions that extend beyond the classical translation-invariant setting. To this end, we develop a unified theoretical framework for loss functions exhibiting locally polynomial structure. This general theory encompasses important examples such as the squared Wasserstein distance, the Sinkhorn divergence and Stein discrepancies, which have gained prominence in modern statistical inference and mach…

An efficient hybrid approach of quantile and expectile regression
Abdellah Atanane, Abdallah Mkhadri, Karim Oualkacha
https://arxiv.org/abs/2510.05268 https://

An efficient hybrid approach of quantile and expectile regression
Quantiles and expectiles are determined by different loss functions: asymmetric least absolute deviation for quantiles and asymmetric squared loss for expectiles. This distinction ensures that quantile regression methods are robust to outliers but somewhat less effective than expectile regression, especially for normally distributed data. However, expectile regression is vulnerable to lack of robustness, especially for heavy-tailed distributions. To address this trade-off between robustness and…

Rectifying Regression in Reinforcement Learning
Alex Ayoub, David Szepesv\'ari, Alireza Baktiari, Csaba Szepesv\'ari, Dale Schuurmans
https://arxiv.org/abs/2510.00885 ht…

Rectifying Regression in Reinforcement Learning
This paper investigates the impact of the loss function in value-based methods for reinforcement learning through an analysis of underlying prediction objectives. We theoretically show that mean absolute error is a better prediction objective than the traditional mean squared error for controlling the learned policy's suboptimality gap. Furthermore, we present results that different loss functions are better aligned with these different regression objectives: binary and categorical cross-entrop…

Moments Matter: Posterior Recovery in Poisson Denoising via Log-Networks
Shirin Shoushtari, Edward P. Chandler, Ulugbek S. Kamilov
https://arxiv.org/abs/2510.07199 https://

Moments Matter: Posterior Recovery in Poisson Denoising via Log-Networks
Poisson denoising plays a central role in photon-limited imaging applications such as microscopy, astronomy, and medical imaging. It is common to train deep learning models for denoising using the mean-squared error (MSE) loss, which corresponds to computing the posterior mean $\mathbb{E}[x \mid y]$. When the noise is Gaussian, Tweedie's formula enables approximation of the posterior distribution through its higher-order moments. However, this connection no longer holds for Poisson denoising: w…