Blackwell's approachability is a framework where two players, the Decision Maker and the Environment, play a repeated game with vector-valued payoffs. The goal of the Decision Maker is to make the average payoff converge to a given set called the target. When this is indeed possible, simple algorithms which guarantee the convergence are known. This abstract tool was successfully used for the construction of optimal strategies in various repeated games, but also found several applications in online learning. By extending an approach proposed by Abernethy et al. (2011), we construct and analyze a class of Follow the Regularized Leader algorithms (FTRL) for Blackwell's approachability which are able to minimize not only the Euclidean distance to the target set (as it is often the case in the context of Blackwell's approachability) but a wide range of distance-like quantities. This flexibility enables us to apply these algorithms to minimize the exact quantity of interest in various online learning problems. In particular, for regret minimization with global costs, we obtain novel guarantees for general norm cost functions, and for the case of $\ell_p$ cost functions, we obtain the first regret bounds with explicit dependence in $p$ and the dimension $d$.

Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn.

A plain playground seems attractive over hundreds of man-hours beautifully chiseled and crafted classroom for a child. Imagine if classrooms were like a playground, the learning would be at its highest. This might not be attractive to educators and parents because of constant pressure of academic readiness. This'attractiveness' isn't supported by volumes of research, play is vital for scientific inquiry and conceptual thinking. Play is fun and voluntary.

Now we calculate the threshold value for every frame and determine the contours using cv2.findContours and return the max contours (the most outermost contours for the object) using the function segment. Using the contours we are able to determine if there is any foreground object being detected in the ROI, in other words, if there is a hand in the ROI. When contours are detected (or hand is present in the ROI), We start to save the image of the ROI in the train and test set respectively for the letter or number we are detecting it for. In the above example, the dataset for 1 is being created and the thresholded image of the ROI is being shown in the next window and this frame of ROI is being saved in ..train/1/example.jpg For the train dataset, we save 701 images for each number to be detected, and for the test dataset, we do the same and create 40 images for each number. Now on the created data set we train a CNN. First, we load the data using ImageDataGenerator of keras through which we can use the flow_from_directory function to load the train and test set data, and each of the names of the number folders will be the class names for the imgs loaded.

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What platform to choose for machine learning development is one of the questions that torments organizations embarking in the space. The rapid growth of the machine learning market has translated…

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.

An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this paper presents a systematic literature review of research at the intersection of SE & DL. The review canvases work appearing in the most prominent SE and DL conferences and journals and spans 84 papers across 22 unique SE tasks. We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a research roadmap that both delineates the foundations of DL techniques applied to SE research, and likely areas of fertile exploration for the future.

In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this paper we extend this setting from current static one-data-set analyses to learners which change their learning algorithm or latent state to improve during learning, and to generalize to new datasets. We introduce a multi-agent formulation in which learners' inner state may change with the teaching interaction, which affects the learning performance in future tasks. In order to teach such learners, we propose an optimal control approach that takes the future performance of the learner after teaching into account. This provides tools for modelling learners having inner states, and machine teaching of meta-learning algorithms. Furthermore, we distinguish manipulative teaching, which can be done by effectively hiding data and also used for indoctrination, from more general education which aims to help the learner become better at generalization and learning in new datasets in the absence of a teacher.