Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.

Devising procedures for auditing generative model privacy-utility tradeoff is an important yet unresolved problem in practice. Existing works concentrates on investigating the privacy constraint side effect in terms of utility degradation of the train on synthetic, test on real paradigm of synthetic data training. We push such understanding on privacy-utility tradeoff to next level by observing the privacy deregulation side effect on synthetic training data utility. Surprisingly, we discover the Utility Recovery Incapability of DP-CTGAN and PATE-CTGAN under privacy deregulation, raising concerns on their practical applications. The main message is Privacy Deregulation does NOT always imply Utility Recovery.

准确，快速的双核细胞（BC）检测在预测白血病和其他恶性肿瘤的风险中起着重要作用。但是，手动显微镜计数是耗时的，缺乏客观性。此外，由于bc显微镜整体幻灯片图像（WSIS）的染色质量和多样性的限制，传统的图像处理方法是无助的。为了克服这一挑战，我们提出了一种基于深度学习的结构启发的两阶段检测方法，该方法是基于深度学习的，该方法是在斑块级别的WSI-Level和细粒度分类处实施BCS粗略检测的级联。粗糙检测网络是基于用于细胞检测的圆形边界框的多任务检测框架，以及用于核检测的中心关键点。圆的表示降低了自由度，与通常的矩形盒子相比，减轻周围杂质的影响，并且在WSI中可能是旋转不变的。检测细胞核中的关键点可以帮助网络感知，并在后来的细粒分类中用于无监督的颜色层分割。精细的分类网络由基于颜色层掩模的监督和基于变压器的关键区域选择模块组成的背景区域抑制模块，其全局建模能力。此外，首先提出了无监督和未配对的细胞质发生器网络来扩展长尾分配数据集。最后，在BC多中心数据集上进行实验。拟议的BC罚款检测方法在几乎所有评估标准中都优于其他基准，从而为诸如癌症筛查等任务提供了澄清和支持。

我们为随机线性匪徒问题提出了一种新的基于自举的在线算法。关键的想法是采用残留的自举勘探，在该探索中，代理商通过重新采样平均奖励估算的残差来估算下一步奖励。我们的算法，随机线性匪徒（\ texttt {linreboot}）的残留bootstrap探索，从其重新采样分布中估算了线性奖励，并以最高的奖励估计拉动了手臂。特别是，我们为理论框架做出了一个理论框架，以使基于自举的探索机制在随机线性匪徒问题中脱颖而出。关键见解是，Bootstrap探索的强度基于在线学习模型和残差的重新采样分布之间的乐观情绪。这样的观察使我们能够证明所提出的\ texttt {linreboot}确保了高概率$ \ tilde {o}（d \ sqrt {n}）$ sub-linear在温和条件下的遗憾。我们的实验支持\ texttt {重新启动}原理在线性匪徒问题的各种公式中的简易概括性，并显示了\ texttt {linreboot}的显着计算效率。

谣言在社交媒体的时代猖獗。谈话结构提供有价值的线索，以区分真实和假声明。然而，现有的谣言检测方法限制为用户响应的严格关系或过度简化对话结构。在这项研究中，为了减轻不相关的帖子施加的负面影响，基本上加强了用户意见的相互作用，首先将谈话线作为无向相互作用图。然后，我们提出了一种用于谣言分类的主导分层图注意网络，其提高了考虑整个社会环境的响应帖子的表示学习，并参加可以在语义上推断目标索赔的帖子。三个Twitter数据集的广泛实验表明，我们的谣言检测方法比最先进的方法实现了更好的性能，并且展示了在早期阶段检测谣言的优异容量。

强化学习的最新出现为使用这些算法计算的参数估计值创造了强大的统计推断方法的需求。现有的在线学习中统计推断的方法仅限于涉及独立采样观察的设置，而现有的强化学习中统计推断方法（RL）仅限于批处理设置。在线引导程序是一种灵活，有效的方法，用于线性随机近似算法中的统计推断，但在涉及Markov噪声（例如RL）的设置中，其功效尚未探索。在本文中，我们研究了在线引导方法在RL中的统计推断的使用。特别是，我们专注于时间差异（TD）学习和梯度TD（GTD）学习算法，它们本身就是马尔可夫噪声下线性随机近似的特殊实例。该方法在策略评估中的统计推断上表明该方法在分布上是一致的，并且包括数值实验，以证明该算法在跨一系列实际RL环境中在统计推断任务上的有效性。

我们提出了一种分布式引导方法，用于同时推断高维大量数据，该数据被许多机器存储和处理。该方法基于通信有效的偏差套索产生$ \ ell_ \ infty $ norm置信区域，我们提出了一种有效的交叉验证方法来调整每种迭代的方法。从理论上讲，我们证明了对通信的数量$ \ tau _ {\ min} $的下限，它值得统计准确性和效率。此外，$ \ tau _ {\ min} $仅与工人数量和固有维度的对数增加，而几乎不变为标称维度。我们通过广泛的仿真研究测试我们的理论，以及基于美国航空公司的按时绩效数据集的半合成数据集上的可变筛选任务。复制数值结果的代码可在GitHub上获得：https：//github.com/skchao74/distributed-bootstrap。

使用始终有效的在线统计学习程序设计动态定价政策是一个重要且尚未解决的问题。最现有的动态定价政策，重点关注所采用的客户选择模型的忠诚度，展示了在定价过程中调整学习统计模型的在线不确定性的有限能力。在本文中，我们提出了一种新颖的方法，可以使用理论担保设计基于动态定价策略的正规化在线统计学习。新方法克服了在线套索程序持续监测的挑战，并具有多种吸引人的财产。特别是，我们做出了决定性观察，即定价决策的始终有效性构建和茁壮成长在线正规方案。我们所提出的在线正则化计划将建议的乐观在线正常化最高似然定价（Oormlp）定价政策具有三大优势：将市场噪声知识编码为定价过程乐观;在线统计学习，以所有决策点的始终有效期以时间均匀的非渐近Oracle不等式信封预测误差过程。这种类型的非渐近推理结果允许我们在实践中设计更具样品有效和强大的动态定价算法。理论上，所提出的OormLP算法利用高维模型的稀疏结构，并在决策范围内确保对数后悔。通过提出一种乐观的在线套索程序，可以根据非渐近鞅浓度的新颖，提出解决过程级别的动态定价问题的乐观在线套索程序来实现这些理论前进。在实验中，我们在不同的合成和实际定价问题设置中评估OormLP，并证明OormLP推进了最先进的方法。

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.