Machines learning techniques plays a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as autoML. In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computer-aided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation. In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data.

We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence. In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we've seen with a goal of triggering an informed conversation about the state of AI and its implication for the future. This edition builds on the inaugural State of AI Report 2018, which can be found here: www.stateof.ai/2018 We consider the following key dimensions in our report: - Research: Technology breakthroughs and their capabilities.

Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.

The most famous single-player card game is 'Klondike', but our ignorance of its winnability percentage has been called "one of the embarrassments of applied mathematics". Klondike is just one of many single-player card games, generically called 'patience' or 'solitaire' games, for which players have long wanted to know how likely a particular game is to be winnable for a random deal. A number of different games have been studied empirically in the academic literature and by non-academic enthusiasts. Here we show that a single general purpose Artificial Intelligence program, called "Solvitaire", can be used to determine the winnability percentage of approximately 30 different single-player card games with a 95\% confidence interval of +/- 0.1\% or better. For example, we report the winnability of Klondike as 81.956% +/- 0.096% (in the 'thoughtful' variant where the player knows the location of all cards), a 30-fold reduction in confidence interval over the best previous result. Almost all our results are either entirely new or represent significant improvements on previous knowledge.

Specifically, we use hunger and loneliness as a basis Foraging [1] is a collective robotics problem that derives to design rules of interaction for the swarm. The paper is biological inspiration from the behavior of ants [2]. Ants organized as follows: In the next section, we first present engaged in foraging, scout for prey, recruit nest mates when the biological foundations that our metaheuristic is founded prey has been located, and work together as a group to upon. We continue by describing the metaheuristic in detail bring back food to the nest. Foraging belongs to a class of and a broader description of the different behaviors exhibited problems known as coverage problems [3].

We present an approach to Bayesian Optimization that allows for robust search strategies over a large class of challenging functions. Our method is motivated by the belief that the trends useful to exploit in search of the optimum typically are a subset of the characteristics of the true objective function. At the core of our approach is the use of a Latent Gaussian Process Regression model that allows us to modulate the input domain with an orthogonal latent space. Using this latent space we can encapsulate local information about each observed data point that can be used to guide the search problem. We show experimentally that our method can be used to significantly improve performance on challenging benchmarks.

The quantum approximate optimization algorithm (QAOA) has rapidly become a cornerstone of contemporary quantum algorithm development. Despite a growing range of applications, only a few results have been developed towards understanding the algorithms ultimate limitations. Here we report that QAOA exhibits a strong dependence on a problem instances constraint to variable ratio - this problem density places a limiting restriction on the algorithms capacity to minimize a corresponding objective function (and hence solve optimization problems). Such 'reachability deficits' persist even in the absence of barren plateaus [McClean et al., 2018] and are outside of the recently reported level-1 QAOA limitations [Hastings 2019]. Building on general numerical experiments, we compare the presence of reachability deficits with analytic solutions of the variational model of Grover's search algorithm.

When building a deep learning project the most common problem we all face is choosing the correct hyperparameters (often known as optimizers). This is critical as the hyperparameters determine the expertise of the machine learning model. In Machine Learning (ML hereafter), a hyperparameter is a configuration variable that's external to the model and whose value is not estimated from the data given. Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner. When an ML algorithm is used for a specific problem, for example when we are using a grid search or a random search algorithm, then we are actually tuning the hyperparameters of the model to discover the values that help us to achieve the most accurate predictions.

When building a deep learning project the most common problem we all face is choosing the correct hyperparameters (often known as optimizers). This is critical as the hyperparameters determine the expertise of the machine learning model. In Machine Learning (ML hereafter), a hyperparameter is a configuration variable that's external to the model and whose value is not estimated from the data given. Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner. When an ML algorithm is used for a specific problem, for example when we are using a grid search or a random search algorithm, then we are actually tuning the hyperparameters of the model to discover the values that help us to achieve the most accurate predictions.