在这项工作中，我们对对象零件进行了一声视觉搜索。给定具有带注释的负担区域的对象的单个参考图像，我们在目标场景中以语义对应部分进行分段。我们提出了Affcorrs，这是一种无监督的模型，结合了预训练的恐龙图像描述符和环相通讯的特性。我们使用affcorrs来找到相应的课内和间单弹部分分割的负担。这项任务比有监督的替代方案更加困难，但是可以通过模仿和辅助远程处理等将来的工作，例如学习能力。

高斯工艺是能够以代表不确定性的方式学习未知功能的机器学习模型，从而促进了最佳决策系统的构建。由于渴望部署新颖的科学领域的高斯过程，一种迅速增长的研究线路集中于建设性地扩展这些模型来处理非欧几里德域，包括黎曼歧管，例如球形和托尔。我们提出了概括这一类的技术，以模拟黎曼歧管上的矢量字段，这在物理科学中的许多应用领域都很重要。为此，我们介绍了构建规范独立核的一般配方，它诱导高斯矢量字段，即矢量值高斯工艺与几何形状相干，从标量值riemannian内核。我们扩展了标准高斯过程培训方法，例如变分推理，以此设置。这使得旨在使用标准方法培训的Riemannian歧管上的矢量值高斯流程，并使它们可以访问机器学习从业者。

随着由于排名引起的复杂几何结构而增加的对象的数量，许多排名数据的算法在计算上变得棘手。部分排名提出了一个额外的挑战，即首选项仅因所有对象的子集而闻名的排名。由于这些原因，最先进的方法无法扩展到现实世界中的应用程序，例如推荐系统。我们通过利用排名数据的几何结构以及有关对象的其他可用信息来解决这一挑战，以根据绘制剪切函数得出用于排名的内核。该图切割内核结合了子解体优化的效率与基于内核方法的理论特性。该图切割内核结合了子解体优化的效率与基于内核方法的理论特性。

学习包括不同对象之间接触的动态系统的物理结构化表示是机器人技术中基于学习的方法的重要问题。黑盒神经网络可以学会大致表示不连续的动态，但是它们通常需要大量数据，并且在预测更长的时间范围时通常会遭受病理行为。在这项工作中，我们使用深层神经网络和微分方程之间的连接来设计一个深网架构家族，以表示对象之间的接触动态。我们表明，这些网络可以从传统上难以实现黑盒方法和最近启发的神经网络的设置中的嘈杂的观察结果中以数据效率的方式学习不连续的联系事件。我们的结果表明，一种理想化的触摸反馈形式（由生物系统严重依赖）是使这一学习问题可以解决的关键组成部分。加上通过网络体系结构引入的电感偏差，我们的技术可以从观测值中准确学习接触动力学。

多边缘最佳运输使人们能够比较多种概率措施，这些措施越来越多地发现在多任务学习问题中的应用。多边缘运输的一个实际限制是测量，样品和维度数量的计算可扩展性。在这项工作中，我们提出了一种基于随机一维投影的多边缘最佳运输范例，其（广义）距离我们术语切片的多边缘Wasserstein距离。为了构建该距离，我们介绍了一维多边缘Kantorovich问题的表征，并使用它来突出切片的多边缘Wasserstein距离的许多属性。特别是，我们表明（i）切片的多边缘Wasserstein距离是一种（概括的）指标，其诱导与标准的Wasserstein距离相同的拓扑，（ii）它承认无维样本复杂度，（iii）是与切片沃斯斯坦度量标准下的双重Centric的问题紧密连接。我们通过说明切片的多边缘Wasserstein对多任务密度估计和多动力增强学习问题的结论。

各种科学和工程领域使用参数化机制模型。工程师和科学家通常可以假设几个竞争模型来解释特定的过程或现象。考虑一个模特歧视设置，我们希望找到最佳机械，动态模型候选者和最佳模型参数估计。通常，若干竞争机械模型可以解释可用数据，因此通过找到最大化模型预测发散的实验设置，可以通过找到最大化模型预测发散的实验设置来实现最佳地收集额外数据的动态实验。我们争论文献中有两种主要方法，用于解决最佳设计问题：（i）分析方法，使用线性和高斯近似来找设计目标的闭合表达式，以及（ii）数据驱动方法，这通常依赖于计算密集的蒙特卡罗技术。 olofsson等人。 （ICML 35,2018）介绍了高斯工艺（GP）替代模型来杂交的分析和数据驱动方法，这允许计算的实验设计，以识别黑盒式模型。在这项研究中，我们证明我们可以扩展现有的动态实验设计方法，以纳入更广泛的问题不确定性。我们还延伸了Olofsson等人。 （2018）使用GP代理模型来辨别动态黑盒式模型的方法。我们在文献中的着名案例研究中评估了我们的方法，并探讨了使用GP代理到近似基于梯度的方法的后果。

Partial differential equations (PDEs) are important tools to model physical systems, and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works like a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDE, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.

Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose three methods to mitigate the negative confounding effects by better disentangling relevance and bias. Empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches.

G-Enum histograms are a new fast and fully automated method for irregular histogram construction. By framing histogram construction as a density estimation problem and its automation as a model selection task, these histograms leverage the Minimum Description Length principle (MDL) to derive two different model selection criteria. Several proven theoretical results about these criteria give insights about their asymptotic behavior and are used to speed up their optimisation. These insights, combined to a greedy search heuristic, are used to construct histograms in linearithmic time rather than the polynomial time incurred by previous works. The capabilities of the proposed MDL density estimation method are illustrated with reference to other fully automated methods in the literature, both on synthetic and large real-world data sets.

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.