人工神经网络（ANNS）可以被视为非线性筛子，其可以比线性筛更有效地近似高维变量的复杂功能。我们调查与经验经济学相关的中等高维协变量的非参数仪器变量（NPIV）模型的各种ANN的计算性能。我们在加权平均衍生物（WAD）上介绍了两个有效的估计和推断方法：具有最佳加权筛分最小距离（OP-OSMD）程序的正交化插件和筛分有效评分。 WAD的两个估计器都使用ANN筛来近似未知的NPIV功能，并且是根 - N渐近正常和一流的等价物。我们提供详细的从业者的配方，以实现有效的程序。这涉及选择未知NPIV的调整参数，包括在两个过程中存在的条件期望和最佳加权函数，而且还可以选择ES过程中未知RIESZ代表的调谐参数。我们比较各种仿真设计的有限样本性能，涉及涉及最多13个连续协变量，不同的非线性和协变量相关的NPIV功能。一些蒙特卡罗调查结果包括：1）调谐和优化在ANN估计中更精细; 2）给定适当调整，有各种架构的ANN估计都可以表现良好; 3）更容易调整ANN-OSMD估计比ANN EAN估算值; 4）用ANN（比样条曲线）估计变得稳定的推论更难以实现; 5）当前实现和近似理论之间存在间隙。最后，我们应用ANN NPIV以多变量协变者在两个经验需求示例中估算平均部分衍生物。

Practitioners use Hidden Markov Models (HMMs) in different problems for about sixty years. Besides, Conditional Random Fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions. First, we show that basic Linear-Chain CRFs (LC-CRFs), considered as different from the HMMs, are in fact equivalent to them in the sense that for each LC-CRF there exists a HMM - that we specify - whom posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers Maximum Posterior Mode (MPM) and Maximum a Posteriori (MAP) used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in Natural Language Processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs was not necessary.

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.

Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, that deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the attack implementation using various attack frameworks. We also explore the potential countermeasures against such attacks. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts. A typical example of such real-world tasks is dual-arm manipulation. Learning to naively solve such tasks with reinforcement learning is often unfeasible due to the sample complexity and exploration requirements growing with the dimensionality of the action and state spaces. Instead, we would like to handle such environments as multi-agent systems and have several agents control parts of the whole. However, decentralizing the generation of actions requires coordination across agents through a channel limited to information central to the task. This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents. We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.

Misinformation spread over social media has become an undeniable infodemic. However, not all spreading claims are made equal. If propagated, some claims can be destructive, not only on the individual level, but to organizations and even countries. Detecting claims that should be prioritized for fact-checking is considered the first step to fight against spread of fake news. With training data limited to a handful of languages, developing supervised models to tackle the problem over lower-resource languages is currently infeasible. Therefore, our work aims to investigate whether we can use existing datasets to train models for predicting worthiness of verification of claims in tweets in other languages. We present a systematic comparative study of six approaches for cross-lingual check-worthiness estimation across pairs of five diverse languages with the help of Multilingual BERT (mBERT) model. We run our experiments using a state-of-the-art multilingual Twitter dataset. Our results show that for some language pairs, zero-shot cross-lingual transfer is possible and can perform as good as monolingual models that are trained on the target language. We also show that in some languages, this approach outperforms (or at least is comparable to) state-of-the-art models.

DeepMind的游戏理论与多代理团队研究多学科学习的几个方面，从计算近似值到游戏理论中的基本概念，再到在富裕的空间环境中模拟社会困境，并在困难的团队协调任务中培训3-D类人动物。我们小组的一个签名目的是使用DeepMind在DeepMind中提供的资源和专业知识，以深入强化学习来探索复杂环境中的多代理系统，并使用这些基准来提高我们的理解。在这里，我们总结了我们团队的最新工作，并提出了一种分类法，我们认为这重点介绍了多代理研究中许多重要的开放挑战。

在过去的十年中，图上的信号处理已成为一个非常活跃的研究领域。具体而言，使用从图形上构建的框架（例如图上的小波）在统计或深度学习中的应用数量显着增加。我们特别考虑通过数据驱动的小波紧密框架方法在图表上进行信号的情况。这种自适应方法基于使用Stein的无偏风险估计校准的阈值，该阈值适合于紧密框架表示。我们可以使用Chebyshev-Jackson多项式近似值将其扩展到大图，从而可以快速计算小波系数，而无需计算laplacian特征性组成。但是，紧密框架的过度本质将白噪声转化为相关的噪声。结果，转换噪声的协方差出现在确定的差异项中，因此需要计算和存储框架，从而导致大图的不切实际计算。为了估计这种协方差，我们基于零均值和单位方差随机变量的快速转换制定和分析蒙特卡洛策略。这种新的数据驱动的denoisisy方法可以在差异隐私中发现自然应用。从真实和模拟数据的大小变化图上进行了全面的性能分析。

我们考虑在平均场比赛中在线加强学习。与现有作品相反，我们通过开发一种使用通用代理的单个样本路径来估算均值场和最佳策略的算法来减轻对均值甲骨文的需求。我们称此沙盒学习为其，因为它可以用作在多代理非合作环境中运行的任何代理商的温暖启动。我们采用了两种时间尺度的方法，在该方法中，平均场的在线固定点递归在较慢的时间表上运行，并与通用代理更快的时间范围内的控制策略更新同时进行。在足够的勘探条件下，我们提供有限的样本收敛保证，从平均场和控制策略融合到平均场平衡方面。沙盒学习算法的样本复杂性为$ \ Mathcal {o}（\ epsilon^{ - 4}）$。最后，我们从经验上证明了沙盒学习算法在交通拥堵游戏中的有效性。