It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

AI is as hot as a laptop with a broken fan--so scorching that some conferences promise to exclude recruiters. As such, there are plenty of organizations motivated to share AI and machine learning information. This overview aims to help you identify the conferences that are worth your time and meet your needs. At first glance, you could use a background in data mining just to sort through all the events that have "artificial Intelligence" in their titles or include AI conference tracks. I winnowed down the offerings based on the quality of speakers, attendees, and networking opportunities.

Amazon announced today that it's making its range of machine learning courses available to all developers signed up to its AWS platform for free. This program was previously available only to Amazon employees, but anyone can now take advantage of it at no charge by signing up to Amazon Web Services' free plan. It includes 30 courses in total, with over 45 hours of course material, videos, and lab tests. The company says its content addresses beginners, advanced developers, and everyone in between. The company is targeting four groups with this program: developers, data scientists, data platform engineers, and business professionals.

With the continuous evolution of its components, 2019 will be considered as the renaissance period of the entire eLearning industry. There are a few trends that will form the future of the educational system. It has become increasingly popular over the past decade, especially among youth. With eLearning, it has become possible to take a lecture even without being physically present in a classroom. With technological advancement, all that is required is a high-speed internet connection to easily access the lectures online, and in the comforts of your home.

Amazon announced today that it's making the machine learning courses it uses to train its engineers available to everybody for free. The coursework is tailored to four major groups -- developers, data scientists, data platform engineers and business professionals -- and it offers both foundational level lessons as well as more advanced instruction. "Each course starts with the fundamentals, and builds on those through real-world examples and labs, allowing developers to explore machine learning through some fun problems we have had to solve at Amazon," Amazon said in the announcement. "Coursework helps consolidate best practices, and demonstrates how to get started on a range of AWS machine learning services, including Amazon SageMaker, AWS DeepLens, Amazon Rekognition, Amazon Lex, Amazon Polly and Amazon Comprehend." More than 30 courses totalling over 45 hours are available as are videos, labs and other relevant documentation initially developed for training Amazon employees.

Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult to train, with mode collapse and oscillations a common problem. We hypothesize that this is at least in part due to the evolution of the generator distribution and the catastrophic forgetting tendency of neural networks, which leads to the discriminator losing the ability to remember synthesized samples from previous instantiations of the generator. Recognizing this, our contributions are twofold. First, we show that GAN training makes for a more interesting and realistic benchmark for continual learning methods evaluation than some of the more canonical datasets. Second, we propose leveraging continual learning techniques to augment the discriminator, preserving its ability to recognize previous generator samples. We show that the resulting methods add only a light amount of computation, involve minimal changes to the model, and result in better overall performance on the examined image and text generation tasks.

Abstract--While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities witha focus on science applications. First, its selfservice modelrepository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published modelsthrough a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3-compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications. I. INTRODUCTION Machine Learning (ML) is disrupting nearly every aspect of computing. Researchers now turn to ML methods to uncover patterns in vast data collections and to make decisions with little or no human input. As ML becomes increasingly pervasive, newsystems are required to support the development, adoption, and application of ML. We refer to the broad class of systems designed to support ML as "learning systems." Learning systems need to support the entire ML lifecycle (see Figure 1), including model development [1, 2]; scalable training across potentially tens of thousands of cores and GPUs [3]; model publication and sharing [4]; and low latency and highthroughput inference[5]; all while encouraging best-practice software engineering when developing models [6].

In bandits, arms' distributions are stationary. This is often violated in practice, where rewards change over time. In applications as recommendation systems, online advertising, and crowdsourcing, the changes may be triggered by the pulls, so that the arms' rewards change as a function of the number of pulls. In this paper, we consider the specific case of non-parametric rotting bandits, where the expected reward of an arm may decrease every time it is pulled. We introduce the filtering on expanding window average (FEWA) algorithm that at each round constructs moving averages of increasing windows to identify arms that are more likely to return high rewards when pulled once more. We prove that, without any knowledge on the decreasing behavior of the arms, FEWA achieves similar anytime problem-dependent, $\widetilde{\mathcal{O}}(\log{(KT)}),$ and problem-independent, $\widetilde{\mathcal{O}}(\sqrt{KT})$, regret bounds of near-optimal stochastic algorithms as UCB1 of Auer et al. (2002a). This result substantially improves the prior result of Levine et al. (2017) which needed knowledge of the horizon and decaying parameters to achieve problem-independent bound of only $\widetilde{\mathcal{O}}(K^{1/3}T^{2/3})$. Finally, we report simulations confirming the theoretical improvements of FEWA.

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains -- Atari, the DeepMind Control Suite and DeepMind Lab.

Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. However, practitioners are faced with a fragmented literature that offers a bewildering array of algorithmic options. First, the variational family. Second, the granularity of the updates e.g. whether the updates are local to each data point and employ message passing or global. Third, the method of optimization (bespoke or blackbox, closed-form or stochastic updates, etc.). This paper presents a new framework, termed Partitioned Variational Inference (PVI), that explicitly acknowledges these algorithmic dimensions of VI, unifies disparate literature, and provides guidance on usage. Crucially, the proposed PVI framework allows us to identify new ways of performing VI that are ideally suited to challenging learning scenarios including federated learning (where distributed computing is leveraged to process non-centralized data) and continual learning (where new data and tasks arrive over time and must be accommodated quickly). We showcase these new capabilities by developing communication-efficient federated training of Bayesian neural networks and continual learning for Gaussian process models with private pseudo-points. The new methods significantly outperform the state-of-the-art, whilst being almost as straightforward to implement as standard VI.