在主要代理模型中，校长向代理商提供了一份合同以执行某项任务。代理商付出了一定的努力，使她的实用性最大化。校长忽略了代理人所选择的努力水平，并且只能根据可能的结果来应对她的工资。在这项工作中，我们考虑了一个模型，其中主体不知道代理商的效用和行动空间：她顺序向相同的代理提供合同，并观察结果的结果。我们提出了一种在温和假设下学习最佳合同的算法。我们约束了本金所需的样本数量，以获取每$ \ eps> 0 $的最佳净利润$ \ eps $以内的合同。即使考虑规避风险的代理，我们的结果也很强。此外，我们表明，当只有两个可能的结果或试剂是风险中性的时，算法的结果近似于经典理论中描述的最佳合同。

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.

When designing a new API for a large project, developers need to make smart design choices so that their code base can grow sustainably. To ensure that new API components are well designed, developers can learn from existing API components. However, the lack of standardized method for comparing API designs makes this learning process time-consuming and difficult. To address this gap we developed the API-Spector, to the best of our knowledge one of the first API-to-API specification recommendation engines. API-Spector retrieves relevant specification components written in OpenAPI (a widely adopted language used to describe web APIs). API-Spector presents several significant contributions, including: (1) novel methods of processing and extracting key information from OpenAPI specifications, (2) innovative feature extraction techniques that are optimized for the highly technical API specification domain, and (3) a novel log-linear probabilistic model that combines multiple signals to retrieve relevant and high quality OpenAPI specification components given a query specification. We evaluate API-Spector in both quantitative and qualitative tasks and achieve an overall of 91.7% recall@1 and 56.2% F1, which surpasses baseline performance by 15.4% in recall@1 and 3.2% in F1. Overall, API-Spector will allow developers to retrieve relevant OpenAPI specification components from a public or internal database in the early stages of the API development cycle, so that they can learn from existing established examples and potentially identify redundancies in their work. It provides the guidance developers need to accelerate development process and contribute thoughtfully designed APIs that promote code maintainability and quality.

We first prove that Littlestone classes, those which model theorists call stable, characterize learnability in a new statistical model: a learner in this new setting outputs the same hypothesis, up to measure zero, with probability one, after a uniformly bounded number of revisions. This fills a certain gap in the literature, and sets the stage for an approximation theorem characterizing Littlestone classes in terms of a range of learning models, by analogy to definability of types in model theory. We then give a complete analogue of Shelah's celebrated (and perhaps a priori untranslatable) Unstable Formula Theorem in the learning setting, with algorithmic arguments taking the place of the infinite.

We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classifiers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact, learn \emph{arbitrary} distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal \emph{labeled} sample complexity of $\tilde{O}(d/\varepsilon)$ labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.

学习曲线将学习算法的预期误差绘制为标记输入样本数量的函数。它们被机器学习实践者广泛使用，以衡量算法的性能，但是经典的PAC学习理论无法解释其行为。在本文中，我们介绍了一种称为VCL维度的新组合表征，该表征改进并完善了Bousquet等人的最新结果。 （2021）。我们的表征通过提供细粒度的边界来展示学习曲线的结构，并表明对于有限VCL的类，可以将衰减的速率分解为仅取决于假设类别和指数成分的线性组件，该成分是指数的成分。还取决于目标分布。特别是，VCL维度的细微差别意味着比Bousquet等人的边界更强大的下限。 （2021年），比经典的“无免费午餐”下界强。 VCL表征解决了Antos and Lugosi（1998）研究的一个开放问题，他们询问在哪些情况下存在这种下限。作为推论，我们在$ \ mathbb {r}^d $中恢复了其下限，并以原则性的方式也适用于其他情况。最后，为了对我们的工作以及与传统PAC学习界的比较提供另一个观点，我们还以一种更接近PAC环境的语言展示了结果的替代表述。

源代码（MLONCODE）上的机器学习有望改变软件的交付方式。通过挖掘软件伪像之间的上下文和关系，mloncode通过代码自动生成，代码建议，代码自动标记和其他数据驱动的增强功能增强了软件开发人员的功能。对于许多任务中，代码的脚本级别表示足够，但是，在许多情况下，要考虑各种依赖关系和存储库结构的存储库级表示，例如，自动标记存储库具有主题或自动记录的存储库。代码等，用于计算存储库级表示的现有方法受（a）依赖代码的自然语言文档（例如，读书文件）（b）方法/脚本级表示的天真聚集，例如，通过串联或平均值。本文介绍了一个深度神经网络，该网络可直接从源代码中生成可公开可用的GitHub代码存储库的存储库嵌入。主题结合了一种注意机制，该机制将源代码，完整依赖关系图和脚本级别的文本信息投射到密集的存储库级表示中。为了计算存储库级别的表示，局部训练可以预测与存储库相关的主题，该主题是在公开可用的GitHub存储库数据集中，这些存储库与他们的地面真相主题标签一起爬行。我们的实验表明，局部计算的嵌入能够胜过多个基线，包括通过在存储库自动标记的任务下平均或串联来天真地结合方法级表示的基线。

准确地估算主要山区盆地中的积雪对于水资源经理来说至关重要，以便做出影响当地和全球经济，野生动植物和公共政策的决策。目前，此估计需要多个配备LIDAR的飞机飞行或原位测量值，两者均昂贵，稀疏和对可访问区域有偏见。在本文中，我们证明了来自多个，公开可用的卫星和天气数据源的空间和时间信息的融合，可以估算关键山区的积雪。我们的多源模型的表现优于单源估计值5.0英寸RMSE，并且优于稀疏的原位测量值的估计值1.2英寸RMSE。

最近有很多不可能的结果表明，在与对抗对手的马尔可夫游戏中最小化的遗憾在统计学上和计算上是棘手的。然而，这些结果都没有排除在所有各方采用相同学习程序的假设下，遗憾最小化的可能性。在这项工作中，我们介绍了第一种（据我们所知）在通用马尔可夫游戏中学习的算法，该算法在所有代理商执行时提供了sublinear后悔保证。我们获得的边界是为了置换遗憾，因此，在此过程中，意味着融合了相关的平衡。我们的算法是分散的，计算上有效的，并且不需要代理之间的任何通信。我们的主要观察结果是，在马尔可夫游戏中通过策略优化的在线学习基本上减少了一种加权遗憾的最小化形式，而未知权重由代理商的策略顺序的路径长度确定。因此，控制路径长度会导致加权的遗憾目标，以提供足够的适应性算法提供统一的后悔保证。