基于特征的自我解释方法解释了他们的分类，从人类可行的特征方面。在医学成像社区中，这种临床知识的语义匹配大大增加了AI的可信度。但是，功能附加注释的成本仍然是一个紧迫的问题。我们通过提出Credanno来解决这个问题，这是一种用于肺结核诊断的数据/注释有效的自我解释方法。 Credanno通过引入自我保护的对比学习来大大减少注释需求，以减轻从注释中学习大多数参数的负担，从而通过两阶段的培训代替端到端的培训。当使用数百个结节样本和仅1％的注释训练时，Credanno在预测恶性肿瘤方面取得了竞争力的准确性，同时，在预测结节属性方面大多数以前的作品都显着超过了。学习空间的可视化进一步表明，恶性肿瘤和结节属性的聚类与临床知识一致。我们的完整代码可用：https：//github.com/ludles/credanno。

我们介绍了一种能够在量子点阵列中找到库仑钻石的方面的算法。我们使用恒定相互作用模型模拟这些阵列，并仅依靠一维栅格扫描（射线）使用正则化最大似然估计来学习设备的模型。这使我们能够确定设备的给定电荷状态，过渡的存在以及它们的补偿门电压是什么。对于较小的设备，模拟器还可以用于计算库仑钻石的确切边界，我们用来评估我们的算法正确地找到了具有高精度的绝大多数过渡。

由胰腺管网络的具有挑战性的分割任务激发，本文解决了两个通常遇到生物医学成像问题的问题：分割的拓扑一致性，以及昂贵或困难的注释。我们的贡献如下：a）我们提出了一个拓扑评分，该评分衡量了预测和地面真理分割之间的拓扑和几何一致性，应用于模型选择和验证。 b）我们在时间序列图像数据上为这一困难的嘈杂任务提供了完整的深度学习方法。在我们的方法中，我们首先使用半监管的U-NET体系结构，适用于通用分割任务，该任务共同训练自动编码器和分割网络。然后，随着时间的流逝，我们使用循环的跟踪来进一步改善预测的拓扑。这种半监督的方法使我们能够利用未经通知的数据来学习特征表示，尽管我们的带注释的培训数据的变化非常有限，但该特征表示具有较高可变性的数据。我们的贡献在具有挑战性的分割任务上得到了验证，从嘈杂的实时成像共聚焦显微镜中定位胎儿胰腺中的管状结构。我们表明，我们的半监督模型不仅优于完全监督和预训练的模型，而且还优于在训练过程中考虑拓扑一致性的方法。此外，与经过平均循环得分为0.762的CLDICE的U-NET相比，我们的方法的平均环路得分为0.808。

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a \emph{generative model}. We propose the AE-LSVI algorithm for best-policy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy \emph{uniformly} over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required.

We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops. We endow each of these sets with a geometric structure, inducing the notions of closeness and symmetries, by turning them into a vertex set of an appropriate metagraph. Building on this, we describe the class of priors that respect this structure and are analogous to the Euclidean isotropic processes, like squared exponential or Mat\'ern. We propose an efficient computational technique for the ostensibly intractable problem of evaluating these priors' kernels, making such Gaussian processes usable within the usual toolboxes and downstream applications. We go further to consider sets of equivalence classes of unweighted graphs and define the appropriate versions of priors thereon. We prove a hardness result, showing that in this case, exact kernel computation cannot be performed efficiently. However, we propose a simple Monte Carlo approximation for handling moderately sized cases. Inspired by applications in chemistry, we illustrate the proposed techniques on a real molecular property prediction task in the small data regime.

Existing generalization bounds fail to explain crucial factors that drive generalization of modern neural networks. Since such bounds often hold uniformly over all parameters, they suffer from over-parametrization, and fail to account for the strong inductive bias of initialization and stochastic gradient descent. As an alternative, we propose a novel optimal transport interpretation of the generalization problem. This allows us to derive instance-dependent generalization bounds that depend on the local Lipschitz regularity of the earned prediction function in the data space. Therefore, our bounds are agnostic to the parametrization of the model and work well when the number of training samples is much smaller than the number of parameters. With small modifications, our approach yields accelerated rates for data on low-dimensional manifolds, and guarantees under distribution shifts. We empirically analyze our generalization bounds for neural networks, showing that the bound values are meaningful and capture the effect of popular regularization methods during training.

In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a suitable probabilistic model can be challenging, however. In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations. Here, we propose a data-driven approach to this problem by meta-learning priors for safe BO from offline data. We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner via empirical uncertainty metrics and a frontier search algorithm. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches while maintaining safety.

平均场游戏（MFG）是建模单个代理人与大量人群随机相互作用的集体行为的关键数学框架。在这项工作中，我们旨在解决一个具有挑战性的MFG类别，在该类别中，这些相互作用的偏好的不同性能可能无法提供给求解器，并敦促人群准确地融合到某些期望的分布中。尽管出于实际目的，这些设置动机良好，但足以使大多数（深）数值求解器瘫痪。然而，我们证明了schr \“作为熵调制的最佳运输模型的奥德桥可以推广到接受平均场结构，因此解决了这些MFG。有趣的是，这导致了一个与时间差异学习相似的结构的计算框架。因此，它为深厚的强化学习开辟了新颖的算法联系，我们利用了促进实践培训。我们表明我们的目标功能提供了必要和足够的功能平均场问题的条件。我们的方法被称为深广泛的Schr \“ Odinger Bridge（DEEPGSB），不仅在解决经典人群导航MFG方面优于先前的方法，而且还能够解决1000维的意见去极化，设置一个新的新观点高维MFG的最先进的数值求解器。我们的代码将在https://github.com/ghliu/deepgsb上提供。

本文描述了我们对第9届论证挖掘研讨会共同任务的贡献（2022）。我们的方法使用大型语言模型来进行论证质量预测的任务。我们使用GPT-3进行及时的工程，并研究培训范式多任务学习，对比度学习和中任务培训。我们发现混合预测设置优于单个模型。提示GPT-3最适合预测论点有效性，而论证新颖性最好通过使用所有三个训练范式训练的模型来估算。