With the development of big data technology, Gradient Boosting Decision Tree, i.e. GBDT, becomes one of the most important machine learning algorithms for its accurate output. However, the training process of GBDT needs a lot of computational resources and time. In order to accelerate the training process of GBDT, the asynchronous parallel sampling gradient boosting decision tree, abbr. asynch-SGBDT is proposed in this paper. Via introducing sampling, we adapt the numerical optimization process of traditional GBDT training process into stochastic optimization process and use asynchronous parallel stochastic gradient descent to accelerate the GBDT training process. Meanwhile, the theoretical analysis of asynch-SGBDT is provided by us in this paper. Experimental results show that GBDT training process could be accelerated by asynch-SGBDT. Our asynchronous parallel strategy achieves an almost linear speedup, especially for high-dimensional sparse datasets.

Guest written by Rebecca Njeri! What is a Decision Tree? Let's start with a story. Suppose you have a business and you want to acquire some new customers. You also have a limited budget, and you want to ensure that, in advertising, you focus on customers who are the most likely to be converted.

Random Differential Equations provide a natural extension of Ordinary Differential Equations to the stochastic setting. We show how, and under which conditions, every equilibrium state of a Random Differential Equation (RDE) can be described by a Structural Causal Model (SCM), while pertaining the causal semantics. This provides an SCM that captures the stochastic and causal behavior of the RDE, which can model both cycles and confounders. This enables the study of the equilibrium states of the RDE by applying the theory and statistical tools available for SCMs, for example, marginalizations and Markov properties, as we illustrate by means of an example. Our work thus provides a direct connection between two fields that so far have been developing in isolation.

Today, the amount of data that can be retrieved from communications networks is extremely high and diverse (e.g., data regarding users behavior, traffic traces, network alarms, signal quality indicators, etc.). Advanced mathematical tools are required to extract useful information from this large set of network data. In particular, Machine Learning (ML) is regarded as a promising methodological area to perform network-data analysis and enable, e.g., automatized network self-configuration and fault management. In this survey we classify and describe relevant studies dealing with the applications of ML to optical communications and networking. Optical networks and system are facing an unprecedented growth in terms of complexity due to the introduction of a huge number of adjustable parameters (such as routing configurations, modulation format, symbol rate, coding schemes, etc.), mainly due to the adoption of, among the others, coherent transmission/reception technology, advanced digital signal processing and to the presence of nonlinear effects in optical fiber systems. Although a good number of research papers have appeared in the last years, the application of ML to optical networks is still in its early stage. In this survey we provide an introductory reference for researchers and practitioners interested in this field. To stimulate further work in this area, we conclude the paper proposing new possible research directions.

There are many different methods for identifying outliers and a lot of them are available in R. But are outliers a matter of opinion? Do all methods give the same results? Articles on outlier methods use a mixture of theory and practice. Theory is all very well, but outliers are outliers because they don't follow theory. Practice involves testing methods on data, sometimes with data simulated based on theory, better with real' datasets.

Health officials are warning thousands of people who attended a national cheerleading competition in Texas last month to be on the lookout for signs and symptoms of mumps. A person with the virus attended the National Cheerleaders Association All-Star National Championship in Fort Worth, which drew crowds from 39 states, between Feb. 23 and Feb. 25. Anyone who was present during that time may have been exposed, the Texas Department of State Health Services (DSHS) said in a letter. No residents in Texas have been reported with the disease as of Tuesday, according to the Dallas News. Here's what you need to know.

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Explainable AI or XAI is a sub-category of AI where the decisions made by the model can be interpreted by humans, as opposed to "black box" models. As AI moves from correcting our spelling and targeting ads to driving our cars and diagnosing patients, the need to verify and justify the conclusions being reached is beginning to be prioritised. To begin to delve into the field, lets look at one simple XAI model: the decision tree. Decision trees can be easily read and even mimic a human approach to decision making by breaking the choice into many small sub-choices. A simple example is how one may evaluate local universities when the leave high school.

We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly control the false detection probability of these CP detection methods because selection bias in each stage must be properly corrected. Our main contribution in this paper is to formulate a CP detection problem as a selective inference problem, and show that exact (non-asymptotic) inference is possible for a class of CP detection methods. We demonstrate the performances of the proposed selective inference framework through numerical simulations and its application to our motivating medical data analysis problem.

Chinese device maker Lepu Medical Technology Co. has received approval from the U.S. Food and Drug Administration for the registration of an electrocardiogram analysis and diagnosis system based on artificial intelligence. The Beijing-based firm's Carewell Healthcare subsidiary developed the product, named AI ECG Platform, which covers major cardiovascular diseases, Lepu said in a statement. AI ECG Platform's diagnostic accuracy for a variety of heart diseases is over 95 percent, a similar level to ECG medical experts and it is capable of outperforming specialists for the diagnosis of some complex cases, the firm added. The company has applied for more than a dozen Chinese and international patents related to the technology. Lepu aims to promote the use of the product in primary hospitals and clinics which lack professional cardiologists.