This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images- and signal-based processing procedures. After transforming a time series data into its corresponding image, the problem of data forecasting becomes essentially a problem of image inpainting problem, i.e., completing missing data in the image. An extensive experimental evaluation is conducted using a large dataset proposed by the well-known M3-competition. Results show that FM2I represents an efficient and robust tool for time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 forecasting methods.

Linear residualization is a common practice for confounding adjustment in machine learning (ML) applications. Recently, causality-aware predictive modeling has been proposed as an alternative causality-inspired approach for adjusting for confounders. The basic idea is to simulate counterfactual data that is free from the spurious associations generated by the observed confounders. In this paper, we compare the linear residualization approach against the causality-aware confounding adjustment in anticausal prediction tasks, and show that the causality-aware approach tends to (asymptotically) outperform the residualization adjustment in terms of predictive performance in linear learners. Importantly, our results still holds even when the true model is not linear. We illustrate our results in both regression and classification tasks, where we compared the causality-aware and residualization approaches using mean squared errors and classification accuracy in synthetic data experiments where the linear regression model is mispecified, as well as, when the linear model is correctly specified. Furthermore, we illustrate how the causality-aware approach is more stable than residualization with respect to dataset shifts in the joint distribution of the confounders and outcome variables.

Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AIpowered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AIbased decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak. Introduction Shortage of physicians and increasing healthcare costs have created a need for digital solutions to better optimize medical resources. In addition, patient expectations for mobile, fast and easy 24/7 access to doctors and health services drive the development of patient-centered solutions.

Although recommender systems have received great success, We categorize recommender systems as shallow it is well known for highly skewed datasets, models and deep models. The first class engineers and researchers need to adjust their incorporates shallow machine learning technologies methods to tackle the specific problem to yield good such as matrix factorization and learning to rank, results. Inability to deal with highly skewed dataset while the second class are deep learning models like usually generates hard computational problems for Wide and Deep [6]. Although a bit of out-of-dated, big data clusters and unsatisfactory results for shallow models are still widely used in small customers. In this paper, we propose a new companies and projects where agility, usability and algorithm solving the problem in the framework of matrix factorization. We model the data skewness efficiency far outweighs boost of performance which factors in the theoretic modeling of the approach is only economically visible for huge datasets. It is with easy to interpret and easy to implement well known since the invention of the first shallow formulas. We prove in experiments our method model, that data skewness and sparsity poses generates comparably favorite results with popular serious challenges for recommender system recommender system algorithms such as Learning performance. The setbacks are two folds: data to Rank, Alternating Least Squares and Deep Matrix skewness causes problems that need special Factorization.

Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is crucial for managing the resources effectively. Temperature estimation is a non-trivial problem due to thermal variations in the data center. Existing solutions for temperature estimation are inefficient due to their computational complexity and lack of accurate prediction. However, data-driven machine learning methods for temperature prediction is a promising approach. In this regard, we collect and study data from a private cloud and show the presence of thermal variations. We investigate several machine learning models to accurately predict the host temperature. Specifically, we propose a gradient boosting machine learning model for temperature prediction. The experiment results show that our model accurately predicts the temperature with the average RMSE value of 0.05 or an average prediction error of 2.38 degree Celsius, which is 6 degree Celsius less as compared to an existing theoretical model. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts. The results show that our algorithm reduces the peak temperature by 6.5 degree Celsius and consumes 34.5% less energy as compared to the baseline algorithm.

Medical AI products require certification before deployment in most jurisdictions. To date, no clear pathways for regulating medical AI exist. I present a methodological guide to the development of a regulatory package which will form part of a certification process. This approach is predicated on the translation between a statistical risk perspective, typical of medical device regulators, and a deep understanding of machine learning methodologies. This work of translation envisages the statistician as the key negotiator between medical device regulators and machine learning experts, allowing them to communicate more clearly, and thus lead to the development of standardised pathways for medical AI regulation.

Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned neural model's behavior allows us to identify training examples most responsible for a given prediction and, therefore, to faithfully explain the output of a black-box model. The most generally applicable existing method is based on influence functions, which scale poorly for larger sample sizes and models. We propose gradient rollback, a general approach for influence estimation, applicable to neural models where each parameter update step during gradient descent touches a smaller number of parameters, even if the overall number of parameters is large. Neural matrix factorization models trained with gradient descent are part of this model class. These models are popular and have found a wide range of applications in industry. Especially knowledge graph embedding methods, which belong to this class, are used extensively. We show that gradient rollback is highly efficient at both training and test time. Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient descent. This establishes that gradient rollback is robustly estimating example influence. We also conduct experiments which show that gradient rollback provides faithful explanations for knowledge base completion and recommender datasets.

Missing data is very common when you do collect data. But it will be a problem when you in the data analysis phase. A common and the best practice at least for me is just ignoring the missing data. Because no matter how good your method to fill the missing pieces, there is always an error introduced by the method. And then the filler data can't be a missing piece of pattern in the data.

A common marketing analytics challenge is to understand consumer behavior and develop customer attributes or archetypes. As organizations get better at tackling this problem, they can activate marketing strategies to incorporate additional customer knowledge into their campaigns. Building customer profiles is now easier than ever with BigQuery ML, using a technique called clustering. In this post, you'll learn how to create segmentation and how to use these audiences for marketing activation. Clustering algorithms can group similar user behavior together to build segmentation used for marketing.

As Gaussian processes are integrated into increasingly complex problem settings, analytic solutions to quantities of interest become scarcer and scarcer. Monte Carlo methods act as a convenient bridge for connecting intractable mathematical expressions with actionable estimates via sampling. Conventional approaches for simulating Gaussian process posteriors view samples as vectors drawn from marginal distributions over process values at a finite number of input location. This distribution-based characterization leads to generative strategies that scale cubically in the size of the desired random vector. These methods are, therefore, prohibitively expensive in cases where high-dimensional vectors - let alone continuous functions - are required. In this work, we investigate a different line of reasoning. Rather than focusing on distributions, we articulate Gaussian conditionals at the level of random variables. We show how this pathwise interpretation of conditioning gives rise to a general family of approximations that lend themselves to fast sampling from Gaussian process posteriors. We analyze these methods, along with the approximation errors they introduce, from first principles. We then complement this theory, by exploring the practical ramifications of pathwise conditioning in a various applied settings.