Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes crucial for risk mitigation and coping with these extreme events. Machine learning techniques can play a critical role in resilience planning through the generation of realistic extreme weather event scenarios that can be used to evaluate possible mitigation actions. This paper proposes a modular framework that relies on interchangeable components to produce extreme weather event scenarios. We discuss possible alternatives for each of the components and show initial results comparing two approaches on the task of generating precipitation scenarios.

Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.

Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods. These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time. In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this few-shot setting. We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance. Moreover, experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information and only allows for exploiting the coarse-grained positional information of entities. Together, our findings challenge the implicit assumptions and inductive biases of prior work and highlight new directions for research in this area.

Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels. With the proliferation of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious. Therefore, the design of a novel cooperative method that is applicable to not only slow fading but also fast fading is increasingly of importance. To this end, we develop and analyze a deep-learning-aided cooperative method coined predictive relay selection (PRS) in this article. It can remarkably improve the quality of CSI through fading channel prediction while retaining the simplicity of ORS by selecting a single opportunistic relay so as to avoid the complexity of multi-relay coordination and synchronization. Information-theoretic analysis and numerical results in terms of outage probability and channel capacity reveal that PRS achieves full diversity gain in slow fading wireless environments and substantially outperforms the existing schemes in fast fading channels. N wireless communications [1], diversity is an important and essential technique, which can effectively combat the effect of multi-path channel fading by means of transmitting redundant signals over independent channels and then combining multiple faded copies at the receiver. Spatial diversity is particularly attractive as it can be easily combined with other forms of diversity and achieve higher diversity order by simply installing more antennas. Because of the constraint on power supply, hardware size, and cost, it is difficult for mobile terminals in cellular systems or wireless nodes in ad hoc networks to exploit spatial diversity at sub-6GHz carrier frequencies. W. Jiang is with German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany, and is also with the University of Kaiserslautern, Germany, (e-mail: wei.jiang@dfki.de). H. D. Schotten is with German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany, and is also with the University of Kaiserslautern, Germany, (e-mail: schotten@eit.uni-kl.de). In such a cooperative network, when a node sends a signal, its neighboring nodes could act as relays to decode-and-forward (DF) or amplify-and-forward (AF) this signal. By combining multiple copied versions of the original signal at the destination, the network achieves cooperative diversity that is equivalent to spatial diversity gained from co-located multi-antenna systems [4].

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents Tango, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. Tango combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Tango in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screen-recordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use Tango. The results reveal that Tango's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, Tango can reduce developer effort by over 60%, illustrating its practicality.

In many applications of machine learning, such as recommender systems, where an input characterizing a user should be matched with a target representing an ordering of a large number of items, accessing fully supervised data (,) is not an option. Instead, one should expect weak information on the target, which could be a list of previously taken (if items are online courses), watched (if items are plays), etc., items by a user characterized by the feature vector. This motivates weakly supervised learning, aiming at learning a mapping from inputs to targets in such a setting where tools from supervised learning can not be applied off-the-shelves. Recent applications of weakly supervised learning showcase impressive results in solving complex tasks such as action retrieval on instructional videos (Miech et al., 2019), image semantic segmentation (Papandreou et al., 2015), salient object detection (Wang et al., 2017), 3D pose estimation (Dabral et al., 2018), text-to-speech synthesis (Jia et al., 2018), to name a few. However, those applications of weakly supervised learning are usually based on clever heuristics, and theoretical foundations of learning from weakly supervised data are scarce, especially when compared to statistical learning literature on supervised learning (Vapnik, 1995; Boucheron et al., 2005; Steinwart and Christmann, 2008). We aim to provide a step in this direction. In this paper, we focus on partial labelling, a popular instance of weak supervision, approached with a structured prediction point of view Ciliberto et al. (2020). We detail this setup in Section 2. Our contributions are organized as follows.

Dimension is an inherent bottleneck to some modern learning tasks, where optimization methods suffer from the size of the data. In this paper, we study non-isotropic distributions of data and develop tools that aim at reducing these dimensional costs by a dependency on an effective dimension rather than the ambient one. Based on non-asymptotic estimates of the metric entropy of ellipsoids -- that prove to generalize to infinite dimensions -- and on a chaining argument, our uniform concentration bounds involve an effective dimension instead of the global dimension, improving over existing results. We show the importance of taking advantage of non-isotropic properties in learning problems with the following applications: i) we improve state-of-the-art results in statistical preconditioning for communication-efficient distributed optimization, ii) we introduce a non-isotropic randomized smoothing for non-smooth optimization. Both applications cover a class of functions that encompasses empirical risk minization (ERM) for linear models.

Understanding the generalization capability of learning algorithms is at the heart of statistical learning theory. In this paper, we investigate the generalization gap of stochastic gradient Langevin dynamics (SGLD), a widely used optimizer for training deep neural networks (DNNs). We derive an algorithm-dependent generalization bound by analyzing SGLD through an information-theoretic lens. Our analysis reveals an intricate trade-off between learning and information dissipation: SGLD learns from data by updating parameters at each iteration while dissipating information from early training stages. Our bound also involves the variance of gradients which captures a particular kind of "sharpness" of the loss landscape. The main proof techniques in this paper rely on strong data processing inequalities--a fundamental concept in information theory--and Otto-Villani's HWI inequality. Finally, we demonstrate our bound through numerical experiments, showing that it can predict the behavior of the true generalization gap. Keywords: Algorithm-dependent generalization bound, non-convex learning, information theory, stochastic gradient Langevin dynamics.

It is well-known that simple short-sighted algorithms, such as gradient descent, generalize well in the over-parameterized learning tasks, due to their implicit regularization. However, it is unknown whether the implicit regularization of these algorithms can be extended to robust learning tasks, where a subset of samples may be grossly corrupted with noise. In this work, we provide a positive answer to this question in the context of robust matrix recovery problem. In particular, we consider the problem of recovering a low-rank matrix from a number of linear measurements, where a subset of measurements are corrupted with large noise. We show that a simple sub-gradient method converges to the true low-rank solution efficiently, when it is applied to the over-parameterized l1-loss function without any explicit regularization or rank constraint. Moreover, by building upon a new notion of restricted isometry property, called sign-RIP, we prove the robustness of the sub-gradient method against outliers in the over-parameterized regime. In particular, we show that, with Gaussian measurements, the sub-gradient method is guaranteed to converge to the true low-rank solution, even if an arbitrary fraction of the measurements are grossly corrupted with noise.

A common problem in science and engineering is that an observed quantity, X, is determined by an unobserved source, S, which one is interested in. Denoting by f the deterministic relationship between X and S, one thus arrives at the equation (1) X f(S) where X is known but both the relation f and the source S are unknown. The premise that the data X is determined by its source S reflects in the assumption that f is a deterministic function, while the premise that S can be completely inferred from X -- i.e. that no information be lost in the process of going from S to X -- is reflected in the assumption that the function f is one-to-one; for simplicity, it is typically also assumed that f is onto. Any function f of this kind will be referred to as a mixing transformation. The central challenge, known as the problem of Blind Source Separation (BSS), then becomes to infer -- or'identify' -- the hidden source S from the given data X: Under which assumptions is it possible to recover the source data S in (1) if only (2) its mixture X is observed? To what extent can such a recovery be achieved and how can it be performed in practice? It is clear that without additional assumptions, the above problem of inference (2) is severely underdetermined: If X and equation (1) is the only information available but both f and S are unknown, then we may generally find infinitely many possible'explanations' ( S, f) for X which all satisfy (1) but are not otherwise meaningfully related to the true explanation (S, f) underlying the data. In many cases, however, this'indeterminacy of S given X with f unknown' can be controlled by imposing certain statistical conditions on the source S . The following simple example illustrates this situation.