In this paper, we approach the problem of uncertainty quantification in deep
learning through a predictive framework, which captures uncertainty in model
parameters by specifying our assumptions about the predictive distribution of
unseen future data. Under this view, we show that deep ensembling
(Lakshminarayanan et al., 2017) is a fundamentally mis-specified model class,
since it assumes that future data are supported on existing observations only
-- a situation rarely encountered in practice. To address this limitation, we
propose MixupMP, a method that constructs a more realistic predictive
distribution using popular data augmentation techniques. MixupMP operates as a
drop-in replacement for deep ensembles, where each ensemble member is trained
on a random simulation from this predictive distribution. Grounded in the
recently-proposed framework of Martingale posteriors (Fong et al., 2023),
MixupMP returns samples from an implicitly defined Bayesian posterior. Our
empirical analysis showcases that MixupMP achieves superior predictive
performance and uncertainty quantification on various image classification
datasets, when compared with existing Bayesian and non-Bayesian approaches.

本文通过预测框架方法来解决深度学习中的不确定性量化问题，提出了一种更为真实的预测分布构建方法 MixupMP，在各种图像分类数据集上相对于现有的贝叶斯和非贝叶斯方法，MixupMP 实现了更优越的预测性能和不确定性量化。

神经网络中使用数据增强的后验不确定性量化

Posterior Uncertainty Quantification in Neural Networks using Data  Augmentation

In this study, we delve into the dynamics of Wordle using data analysis and
machine learning. Our analysis initially focused on the correlation between the
date and the number of submitted results. Due to initial popularity bias, we
modeled stable data using an ARIMAX model with coefficient values of 9, 0, 2,
and weekdays/weekends as the exogenous variable. We found no significant
relationship between word attributes and hard mode results.
To predict word difficulty, we employed a Backpropagation Neural Network,
overcoming overfitting via feature engineering. We also used K-means
clustering, optimized at five clusters, to categorize word difficulty
numerically. Our findings indicate that on March 1st, 2023, around 12,884
results will be submitted and the word "eerie" averages 4.8 attempts, falling
into the hardest difficulty cluster.
We further examined the percentage of loyal players and their propensity to
undertake daily challenges. Our models underwent rigorous sensitivity analyses,
including ADF, ACF, PACF tests, and cross-validation, confirming their
robustness. Overall, our study provides a predictive framework for Wordle
gameplay based on date or a given five-letter word. Results have been
summarized and submitted to the Puzzle Editor of the New York Times.

通过数据分析和机器学习研究 Wordle 的动态，我们发现日期与提交结果数量之间存在相关性，利用 ARIMAX 模型进行数据稳定化预测。我们使用 BP 神经网络预测单词难度，并利用 K-means 聚类对难度进行数值化分类。研究结果提供了基于日期或给定五个字母单词的 Wordle 游戏玩法的预测框架。

高稳健性 Wordle 游戏结果预测模型

Prediction Model For Wordle Game Results With High Robustness

In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been
commissioned by researchers to enable accurate, online wildfire coverage and
tracking. While the majority of prior work focuses on the coordination and
control of such multi-robot systems, to date, these UAV teams have not been
given the ability to reason about a fire's track (i.e., location and
propagation dynamics) to provide performance guarantee over a time horizon.
Motivated by the problem of aerial wildfire monitoring, we propose a predictive
framework which enables cooperation in multi-UAV teams towards collaborative
field coverage and fire tracking with probabilistic performance guarantee. Our
approach enables UAVs to infer the latent fire propagation dynamics for
time-extended coordination in safety-critical conditions. We derive a set of
novel, analytical temporal, and tracking-error bounds to enable the UAV-team to
distribute their limited resources and cover the entire fire area according to
the case-specific estimated states and provide a probabilistic performance
guarantee. Our results are not limited to the aerial wildfire monitoring
case-study and are generally applicable to problems, such as search-and-rescue,
target tracking and border patrol. We evaluate our approach in simulation and
provide demonstrations of the proposed framework on a physical multi-robot
testbed to account for real robot dynamics and restrictions. Our quantitative
evaluations validate the performance of our method accumulating 7.5x and 9.0x
smaller tracking-error than state-of-the-art model-based and reinforcement
learning benchmarks, respectively.

我们提出了一种预测性框架，使无人机团队能够在协同场地覆盖和火灾跟踪方面进行合作，并提供概率性能保证。

具有服务质量保证的多无人机协同野火覆盖和跟踪规划

Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

One of the main challenges online social systems face is the prevalence of
antisocial behavior, such as harassment and personal attacks. In this work, we
introduce the task of predicting from the very start of a conversation whether
it will get out of hand. As opposed to detecting undesirable behavior after the
fact, this task aims to enable early, actionable prediction at a time when the
conversation might still be salvaged.
To this end, we develop a framework for capturing pragmatic devices---such as
politeness strategies and rhetorical prompts---used to start a conversation,
and analyze their relation to its future trajectory. Applying this framework in
a controlled setting, we demonstrate the feasibility of detecting early warning
signs of antisocial behavior in online discussions.

本研究旨在提出一种初始预测在线讨论是否会演变成骚扰或人身攻击的框架，该框架可以捕捉与未来在线讨论轨迹相关的优雅用语等语言特征，并在受控环境中展示了检测在线讨论的早期预警迹象的可行性。