Symbol Description Vt Set of observable variables at time t V0:TUnion of observable variables at time t= 0,...,T Xt Manipulative variables at time t Yt Target variable at time t P(Xt) Power set of Xt Mt Set of MIS sets at time t Xs,ts-th intervention set at time t In this section we give the proof for Theorem 1 in the main text. This means that W includes those variables that are parents of Yt but are nor target at previous time steps nor intervened variables. In the following proof the values of IV0:t 1, XPYs,t, IPY0:t 1 and W are denoted by i, xPY, iPY and w respectively. Finally, fYY and fNYYare the functions in the SCM for Yt (see Assumptions (1) in the main text). Eq. (2) follows from the Eq. Finally, noticing that p(yPTt |I0:t 1) is the distribution targeted when optimizing the objective function at previous time steps one can obtain Eq. (6). The derivations above show how the objective function at time t is given by the expected value of the output of the functional relationship fNYYwhere the expectation is taken with respect to the variables that are not intervened on. This expectation is then shifted to account for the interventions implemented in the system at previous time steps that are affecting the target variable through fYY .

Adversarial examples for neural network image classifiers are known to be transferable: examples optimized to be misclassified by a source classifier are often misclassified as well by classifiers with different architectures. However, targeted adversarial examples--optimized to be classified as a chosen target class--tend to be less transferable between architectures. While prior research on constructing transferable targeted attacks has focused on improving the optimization procedure, in this work we examine the role of the source classifier. Here, we show that training the source classifier to be "slightly robust"--that is, robust to small-magnitude adversarial examples--substantially improves the transferability of class-targeted and representation-targeted adversarial attacks, even between architectures as different as convolutional neural networks and transformers. The results we present provide insight into the nature of adversarial examples as well as the mechanisms underlying so-called "robust" classifiers.

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query.

Modeling directed graphs with differentiable representations is a fundamental requirement for performing machine learning on graph-structured data.

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This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Toyota's CUE7 robot shoots hoops using AI You don't need an SSN to open a credit card: Scammers know that Mexico's climate supercomputer could change forecasting Watters' Cooler: America got catfished US has to'get creative' in combat in Iranian waters: Joey Jones Michael Easter and Gary Brecka discuss the'choice' to live to be 100 Microsoft Anthropic's Mythos AI found over 2,000 unknown software vulnerabilities in just seven weeks of testing Fox News Flash top headlines are here. Check out what's clicking on FoxNews.com.

Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is still a big challenge in learning authentic "universals" for any two languages. In this paper, we propose EMMA-X: an EM-like Multilingual pre-training Algorithm, to learn (X)Cross-lingual universals with the aid of excessive multilingual non-parallel data.