In regression tasks, aleatoric uncertainty is commonly addressed by considering a parametric distribution of the output variable, which is based on strong assumptions such as symmetry, unimodality or by supposing a restricted shape. These assumptions are too limited in scenarios where complex shapes, strong skews or multiple modes are present. In this paper, we propose a generic deep learning framework that learns an Uncountable Mixture of Asymmetric Laplacians (UMAL), which will allow us to estimate heterogeneous distributions of the output variable and we show its connections to quantile regression. Despite having a fixed number of parameters, the model can be interpreted as an infinite mixture of components, which yields a flexible approximation for heterogeneous distributions. Apart from synthetic cases, we apply this model to room price forecasting and to predict financial operations in personal bank accounts. We demonstrate that UMAL produces proper distributions, which allows us to extract richer insights and to sharpen decision-making.

First of all, we would like to thank all reviewers for their suggestions to improve our paper submission. Reviewers #1 and #2 suggest experiments to measure if UMAL yields calibrated outputs. The mean and standard deviation for all folds of the mean absolute error between the predicted calibration and the perfect ideal calibration is represented in the table. These results restate that UMAL is always in the best positions. ALD tries to estimate, in a non-point-wise manner, their corresponding quantile (Rev.

In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective function. We exhibit and compute a polynomial cost of adaptation to the Hölder regularity for regret minimization. To do this, we first reconsider the recent lower bound of Locatelli and Carpentier [21], and define and characterize admissible rate functions. Our new algorithm matches any of these minimal rate functions. We provide a finite-time analysis and a thorough discussion about asymptotic optimality.

We thank the reviewers for their overall positive and constructive comments. This paper completes the picture in the minimax Hölder setting. Moreover, the Hölder assumption (stated under various names) is standard in this line of work, e.g., in That said, we acknowledge the technicality of the paper. The algorithms were run 30 times and the error bars are 1.96 times the standard deviation. R1: "The horizon T is assumed to be a prior knowledge. This should be stated and commented [...]. Indeed, Subsection 3.3 and Appendix B discuss this and describe how we can get rid this requirement. In the final version we will recall that by "anytime" we mean without the knowledge of T. R2 "Can this algorithmic technique deal with cases in which the function is spatially inhomogenous, This is a good point.

The advent of foundation models (FMs) in healthcare offers unprecedented opportunities to enhance medical diagnostics through automated classification and segmentation tasks. However, these models also raise significant concerns about their fairness, especially when applied to diverse and underrepresented populations in healthcare applications. Currently, there is a lack of comprehensive benchmarks, standardized pipelines, and easily adaptable libraries to evaluate and understand the fairness performance of FMs in medical imaging, leading to considerable challenges in formulating and implementing solutions that ensure equitable outcomes across diverse patient populations. To fill this gap, we introduce FairMedFM, a fairness benchmark for FM research in medical imaging.

We study the proportional clustering problem of Chen et al. (ICML'19) and relate it to the area of multiwinner voting in computational social choice. We show that any clustering satisfying a weak proportionality notion of Brill and Peters (EC'23) simultaneously obtains the best known approximations to the proportional fairness notion of Chen et al., but also to individual fairness (Jung et al., FORC'20) and the "core" (Li et al., ICML'21). In fact, we show that any approximation to proportional fairness is also an approximation to individual fairness and vice versa. Finally, we also study stronger notions of proportional representation, in which deviations do not only happen to single, but multiple candidate centers, and show that stronger proportionality notions of Brill and Peters imply approximations to these stronger guarantees.

We thank reviewers for detailed and helpful reviews. Table 1 shows the results. If we understand correctly, R2's main concern is that the word embeddings of We believe that it would hardly happen. The reasons are as follows. Second, we can easily assume a FSL scenario in which we have access to the labels of the test set.

Ukrainian forces destroyed dozens of Russian warplanes with a drone attack on air bases deep within Russian territory on Sunday. Ukrainian forces destroyed 40 aircraft in the attack, which an official says took more than a year to orchestrate. Russia's defense ministry confirmed the attack on Sunday, saying it struck five airfields. The operation saw drones transported in containers carried by trucks deep into Russian territory, he said. The drones reportedly hit 41 planes stationed at several airfields on Sunday afternoon, including A-50, Tu-95 and Tu-22M aircraft, the official said.

Classical worst-case optimization theory neither explains the success of optimization in machine learning, nor does it help with step size selection. In this paper we demonstrate the viability and advantages of replacing the classical'convex function' framework with a'random function' framework.

Scene flow estimation is an essential ingredient for a variety of real-world applications, especially for autonomous agents, such as self-driving cars and robots. While recent scene flow estimation approaches achieve reasonable accuracy, their applicability to real-world systems additionally benefits from a reliability measure. Aiming at improving accuracy while additionally providing an estimate for uncertainty, we propose DiffSF that combines transformer-based scene flow estimation with denoising diffusion models. In the diffusion process, the ground truth scene flow vector field is gradually perturbed by adding Gaussian noise. In the reverse process, starting from randomly sampled Gaussian noise, the scene flow vector field prediction is recovered by conditioning on a source and a target point cloud. We show that the diffusion process greatly increases the robustness of predictions compared to prior approaches resulting in state-of-the-art performance on standard scene flow estimation benchmarks. Moreover, by sampling multiple times with different initial states, the denoising process predicts multiple hypotheses, which enables measuring the output uncertainty, allowing our approach to detect a majority of the inaccurate predictions.