This assumes that entities and links can be represented as a graph, where entities are nodes and links (symmetric relationships) are edges (arcs if relationships are asymmetric). This prediction problem has been most probably defined for the first time in the social network analysis community [1], however, it has soon become an important problem in other domains, and in particular in large-scale knowledge-bases [2], where it is used to add missing data and discover new facts. When we are dealing with the link prediction problem for knowledge-bases, the semantic information contained within is usually encoded as a knowledge graph (KG) [3]. For the purpose of this manuscript, we treat a knowledge graph as a graph where links may have different types, and we conform to the closed-world assumption. This means that all the existing (asserted) links are considered positive, and all the links which are unknown, and obtained via knowledge graph completion, are considered negative (Figure 1).

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.

Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but they are difficult to be deployed on hardware due to the intensive computation. To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search. We first construct a large design space with $\textit{arbitrary encoder-decoder attention}$ and $\textit{heterogeneous layers}$. Then we train a $\textit{SuperTransformer}$ that covers all candidates in the design space, and efficiently produces many $\textit{SubTransformers}$ with weight sharing. Finally, we perform an evolutionary search with a hardware latency constraint to find a specialized $\textit{SubTransformer}$ dedicated to run fast on the target hardware. Extensive experiments on four machine translation tasks demonstrate that HAT can discover efficient models for different hardware (CPU, GPU, IoT device). When running WMT'14 translation task on Raspberry Pi-4, HAT can achieve $\textbf{3}\times$ speedup, $\textbf{3.7}\times$ smaller size over baseline Transformer; $\textbf{2.7}\times$ speedup, $\textbf{3.6}\times$ smaller size over Evolved Transformer with $\textbf{12,041}\times$ less search cost and no performance loss. HAT code is https://github.com/mit-han-lab/hardware-aware-transformers.git

We study a model for adversarial classification based on distributionally robust chance constraints. We show that under Wasserstein ambiguity, the model aims to minimize the conditional value-at-risk of the distance to misclassification, and we explore links to previous adversarial classification models and maximum margin classifiers. We also provide a reformulation of the distributionally robust model for linear classifiers, and show it is equivalent to minimizing a regularized ramp loss. Numerical experiments show that, despite the nonconvexity, standard descent methods appear to converge to the global minimizer for this problem. Inspired by this observation, we show that, for a certain benign distribution, the regularized ramp loss minimization problem has a single stationary point, at the global minimizer.

The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: Can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game? In order to initiate the research on this question, we introduce the study of an individual's action prediction in a one-shot game based on free text he/she provides, while being unaware of the game to be played. We approach the problem by attributing commonsensical personality attributes via crowd-sourcing to free texts written by individuals, and employing transductive learning to predict actions taken by these individuals in one-shot games based on these attributes. Our approach allows us to train a single classifier that can make predictions with respect to actions taken in multiple games. In experiments with three well-studied games, our algorithm compares favorably with strong alternative approaches. In ablation analysis, we demonstrate the importance of our modeling choices--the representation of the text with the commonsensical personality attributes and our classifier--to the predictive power of our model.

Recognizing sounds is a key aspect of computational audio scene analysis and machine perception. In this paper, we advocate that sound recognition is inherently a multi-modal audiovisual task in that it is easier to differentiate sounds using both the audio and visual modalities as opposed to one or the other. We present an audiovisual fusion model that learns to recognize sounds from weakly labeled video recordings. The proposed fusion model utilizes an attention mechanism to dynamically combine the outputs of the individual audio and visual models. Experiments on the large scale sound events dataset, AudioSet, demonstrate the efficacy of the proposed model, which outperforms the single-modal models, and state-of-the-art fusion and multi-modal models. We achieve a mean Average Precision (mAP) of 46.16 on Audioset, outperforming prior state of the art by approximately +4.35 mAP (relative: 10.4%).

Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are may not adjacent are always neglected. Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step Markov transition probability for Feature Selection). The idea is using multi-step Markov transition probability to describe the relation between any data pair. Two ways from the positive and negative viewpoints are employed respectively to keep the data structure after feature selection. From the positive viewpoint, the maximum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. Then, the features which can keep the compact data structure are selected. From the viewpoint of negative, the minimum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. On the contrary, the features that least maintain the loose data structure are selected. And the two ways can also be combined. Thus three algorithms are proposed. Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure. The performance of our approach is compared with the state-of-the-art methods on eight real-world data sets, and the experimental results show that the proposed MMFS is effective in unsupervised feature selection.

The statistical imprecision of quarterly gross domestic product (GDP) estimates, along with the fact that the first estimate is available with a delay of nearly a month, pose a significant challenge to policy makers, market participants, and other observers with an interest in monitoring the state of the economy in real time; see, e.g., Ghysels, Horan, and Moench (2018) for a recent discussion of macroeconomic data revision and publication delays. A term originated in meteorology, nowcasting pertains to the prediction of the present and very near future. Nowcasting is intrinsically a mixed frequency data problem as the object of interest is a low-frequency data series (e.g., quarterly GDP), whereas the real-time information (e.g., daily, weekly, or monthly) can be used to update the state, or to put it differently, to nowcast the low-frequency series of interest. Traditional methods used for nowcasting rely on dynamic factor models that treat the underlying low frequency series of interest as a latent process with high frequency data noisy observations. These models are naturally cast in a state-space form and inference can be performed using likelihood-based methods and Kalman filtering techniques; see Bańbura, Giannone, Modugno, and Reichlin (2013) for a recent survey.

We explore why many recently proposed robust estimation problems are efficiently solvable, even though the underlying optimization problems are non-convex. We study the loss landscape of these robust estimation problems, and identify the existence of "generalized quasi-gradients". Whenever these quasi-gradients exist, a large family of low-regret algorithms are guaranteed to approximate the global minimum; this includes the commonly-used filtering algorithm. For robust mean estimation of distributions under bounded covariance, we show that any first-order stationary point of the associated optimization problem is an {approximate global minimum} if and only if the corruption level $\epsilon < 1/3$. Consequently, any optimization algorithm that aproaches a stationary point yields an efficient robust estimator with breakdown point $1/3$. With careful initialization and step size, we improve this to $1/2$, which is optimal. For other tasks, including linear regression and joint mean and covariance estimation, the loss landscape is more rugged: there are stationary points arbitrarily far from the global minimum. Nevertheless, we show that generalized quasi-gradients exist and construct efficient algorithms. These algorithms are simpler than previous ones in the literature, and for linear regression we improve the estimation error from $O(\sqrt{\epsilon})$ to the optimal rate of $O(\epsilon)$ for small $\epsilon$ assuming certified hypercontractivity. For mean estimation with near-identity covariance, we show that a simple gradient descent algorithm achieves breakdown point $1/3$ and iteration complexity $\tilde{O}(d/\epsilon^2)$.

Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has generated significant interest in recent years. Apart from being a tool of general convenience, automated code repair has significant pedagogical applications for novice programmers who find compiler error messages cryptic and unhelpful. Existing approaches largely solve this problem using a blackbox-application of a heavy-duty generative learning technique, such as sequence-to-sequence prediction (TRACER) or reinforcement learning (RLAssist). Although convenient, such black-box application of learning techniques makes existing approaches bulky in terms of training time, as well as inefficient at targeting specific error types. We present MACER, a novel technique for accelerated error repair based on a modular segregation of the repair process into repair identification and repair application. MACER uses powerful yet inexpensive discriminative learning techniques such as multi-label classifiers and rankers to first identify the type of repair required and then apply the suggested repair. Experiments indicate that the fine-grained approach adopted by MACER offers not only superior error correction, but also much faster training and prediction. On a benchmark dataset of 4K buggy programs collected from actual student submissions, MACER outperforms existing methods by 20% at suggesting fixes for popular errors that exactly match the fix desired by the student. MACER is also competitive or better than existing methods at all error types -- whether popular or rare. MACER offers a training time speedup of 2x over TRACER and 800x over RLAssist, and a test time speedup of 2-4x over both.