Since Intersection-over-Union (IoU) based optimization maintains the
consistency of the final IoU prediction metric and losses, it has been widely
used in both regression and classification branches of single-stage 2D object
detectors. Recently, several 3D object detection methods adopt IoU-based
optimization and directly replace the 2D IoU with 3D IoU. However, such a
direct computation in 3D is very costly due to the complex implementation and
inefficient backward operations. Moreover, 3D IoU-based optimization is
sub-optimal as it is sensitive to rotation and thus can cause training
instability and detection performance deterioration. In this paper, we propose
a novel Rotation-Decoupled IoU (RDIoU) method that can mitigate the
rotation-sensitivity issue, and produce more efficient optimization objectives
compared with 3D IoU during the training stage. Specifically, our RDIoU
simplifies the complex interactions of regression parameters by decoupling the
rotation variable as an independent term, yet preserving the geometry of 3D
IoU. By incorporating RDIoU into both the regression and classification
branches, the network is encouraged to learn more precise bounding boxes and
concurrently overcome the misalignment issue between classification and
regression. Extensive experiments on the benchmark KITTI and Waymo Open Dataset
validate that our RDIoU method can bring substantial improvement for the
single-stage 3D object detection.

本文提出一种新的旋转解耦 IoU (RDIoU) 方法，通过将旋转变量作为独立项来简化回归参数的复杂交互，并在回归和分类分支中应用 RDIoU，从而可以提高单级 3D 目标检测的准确性和稳定性，并在 KITTI 和 Waymo 开放数据集的广泛实验中验证，此方法可以带来相当大的改进。

单阶段三维物体检测中基于 IoU 的优化的再思考

Rethinking IoU-based Optimization for Single-stage 3D Object Detection

Motivated by the application of real-time pricing in e-commerce platforms, we
consider the problem of revenue-maximization in a setting where the seller can
leverage contextual information describing the customer's history and the
product's type to predict her valuation of the product. However, her true
valuation is unobservable to the seller, only binary outcome in the form of
success-failure of a transaction is observed. Unlike in usual contextual bandit
settings, the optimal price/arm given a covariate in our setting is sensitive
to the detailed characteristics of the residual uncertainty distribution. We
develop a semi-parametric model in which the residual distribution is
non-parametric and provide the first algorithm which learns both regression
parameters and residual distribution with $\tilde O(\sqrt{n})$ regret. We
empirically test a scalable implementation of our algorithm and observe good
performance.

基于实时定价与上下文信息，本文提出了一种半参数模型，能够通过回归参数和残差分布来最大化收益，考虑到了细节特征。

半参数动态情境定价

Semi-parametric dynamic contextual pricing

Big data can easily be contaminated by outliers or contain variables with
heavy-tailed distributions, which makes many conventional methods inadequate.
To address this challenge, we propose the adaptive Huber regression for robust
estimation and inference. The key observation is that the robustification
parameter should adapt to the sample size, dimension and moments for optimal
tradeoff between bias and robustness. Our theoretical framework deals with
heavy-tailed distributions with bounded $(1+\delta)$-th moment for any $\delta
> 0$. We establish a sharp phase transition for robust estimation of regression
parameters in both low and high dimensions: when $\delta \geq 1$, the estimator
admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on
the data, while only a slower rate is available in the regime $0<\delta< 1$.
Furthermore, this transition is smooth and optimal. In addition, we extend the
methodology to allow both heavy-tailed predictors and observation noise.
Simulation studies lend further support to the theory. In a genetic study of
cancer cell lines that exhibit heavy-tailedness, the proposed methods are shown
to be more robust and predictive.

本篇论文提出了一种针对大数据中的尾部离群值和重尾分布等问题的自适应 Huber 回归方法，其中关键观察是，用于鲁棒性参数自适应的最优偏差和鲁棒性之间的权衡应该适应于样本大小，维度和矩。