A key task in actuarial modelling involves modelling the distributional
properties of losses. Classic (distributional) regression approaches like
Generalized Linear Models (GLMs; Nelder and Wedderburn, 1972) are commonly
used, but challenges remain in developing models that can (i) allow covariates
to flexibly impact different aspects of the conditional distribution, (ii)
integrate developments in machine learning and AI to maximise the predictive
power while considering (i), and, (iii) maintain a level of interpretability in
the model to enhance trust in the model and its outputs, which is often
compromised in efforts pursuing (i) and (ii). We tackle this problem by
proposing a Distributional Refinement Network (DRN), which combines an
inherently interpretable baseline model (such as GLMs) with a flexible neural
network-a modified Deep Distribution Regression (DDR; Li et al., 2019) method.
Inspired by the Combined Actuarial Neural Network (CANN; Schelldorfer and
W{\''u}thrich, 2019), our approach flexibly refines the entire baseline
distribution. As a result, the DRN captures varying effects of features across
all quantiles, improving predictive performance while maintaining adequate
interpretability. Using both synthetic and real-world data, we demonstrate the
DRN's superior distributional forecasting capacity. The DRN has the potential
to be a powerful distributional regression model in actuarial science and
beyond.

提出了一种 Distributional Refinement Network (DRN) 模型，结合了 Generalized Linear Models（GLMs）和 Deep Distribution Regression (DDR) 方法，能够灵活地捕捉特征在所有分位点上的不同影响，提高预测性能并保持充分解释性，展示了在保险科学等领域的潜力。

分布式细化网络：通过深度学习进行分布式预测

Distributional Refinement Network: Distributional Forecasting via Deep  Learning

3D object detection is a fundamental task in scene understanding. Numerous
research efforts have been dedicated to better incorporate Hough voting into
the 3D object detection pipeline. However, due to the noisy, cluttered, and
partial nature of real 3D scans, existing voting-based methods often receive
votes from the partial surfaces of individual objects together with severe
noises, leading to sub-optimal detection performance. In this work, we focus on
the distributional properties of point clouds and formulate the voting process
as generating new points in the high-density region of the distribution of
object centers. To achieve this, we propose a new method to move random 3D
points toward the high-density region of the distribution by estimating the
score function of the distribution with a noise conditioned score network.
Specifically, we first generate a set of object center proposals to coarsely
identify the high-density region of the object center distribution. To estimate
the score function, we perturb the generated object center proposals by adding
normalized Gaussian noise, and then jointly estimate the score function of all
perturbed distributions. Finally, we generate new votes by moving random 3D
points to the high-density region of the object center distribution according
to the estimated score function. Extensive experiments on two large scale
indoor 3D scene datasets, SUN RGB-D and ScanNet V2, demonstrate the superiority
of our proposed method. The code will be released at
this https URL

通过估计点云分布的得分函数，将随机的 3D 点移动到物体中心分布的高密度区域，以改善 3D 物体检测的性能。

通过投票步骤扩散从点云中进行的 3D 物体检测

3D Object Detection from Point Cloud via Voting Step Diffusion

We develop differentially private methods for estimating various
distributional properties. Given a sample from a discrete distribution $p$,
some functional $f$, and accuracy and privacy parameters $\alpha$ and
$\varepsilon$, the goal is to estimate $f(p)$ up to accuracy $\alpha$, while
maintaining $\varepsilon$-differential privacy of the sample.
We prove almost-tight bounds on the sample size required for this problem for
several functionals of interest, including support size, support coverage, and
entropy. We show that the cost of privacy is negligible in a variety of
settings, both theoretically and experimentally. Our methods are based on a
sensitivity analysis of several state-of-the-art methods for estimating these
properties with sublinear sample complexities.

本论文提出了一种隐私保护的差分私有方法，基于几种最先进的方法进行灵敏度分析，以实现估计分布属性方面的准确性，同时保持样本的 ε- 差分隐私，并在几种感兴趣的功能上证明了问题所需的样本大小的近乎严格的边界。