Linear regression is a simple machine learning model and chances are you are already aware of it! Do you remember plotting the line y mx c in your introductory algebra class? This is an equation of a straight line where m is its gradient and c is the point where the line crosses the y-axis. Using this equation, you're able to estimate the value of y for any given value of x. Similarly, linear regression involves estimating the relationship between independent variables (x) and a dependent variable(y).

Markov Chain Monte Carlo (MCMC) methods are the workhorse of Bayesian computation when closed formulas for estimators or probability distributions are not available. For this reason they have been central to the development and success of high-dimensional Bayesian statistics in the last decades, where one attempts to generate samples from some posterior distribution Π( |data) arising from a prior Π on D-dimensional Euclidean space and the observed data vector. MCMC methods tend to perform well in a large variety of problems, are very flexible and user-friendly, and enjoy many theoretical guarantees. Under mild assumptions, they are known to converge to their stationary'target' distributions as a consequence of the ergodic theorem, albeit perhaps at a slow speed, requiring a large number of iterations to provide numerically accurate algorithms. When the target distribution is log-concave, MCMC algorithms are known to mix rapidly, even in high dimensions.

Nowadays, machine learning models and applications have become increasingly pervasive. With this rapid increase in the development and employment of machine learning models, a concern regarding privacy has risen. Thus, there is a legitimate need to protect the data from leaking and from any attacks. One of the strongest and most prevalent privacy models that can be used to protect machine learning models from any attacks and vulnerabilities is differential privacy (DP). DP is strict and rigid definition of privacy, where it can guarantee that an adversary is not capable to reliably predict if a specific participant is included in the dataset or not. It works by injecting a noise to the data whether to the inputs, the outputs, the ground truth labels, the objective functions, or even to the gradients to alleviate the privacy issue and protect the data. To this end, this survey paper presents different differentially private machine learning algorithms categorized into two main categories (traditional machine learning models vs. deep learning models). Moreover, future research directions for differential privacy with machine learning algorithms are outlined.

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.

A method for estimating the conditional average treatment effect under condition of censored time-to-event data called BENK (the Beran Estimator with Neural Kernels) is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form called the neural kernels. The conditional average treatment effect is estimated by using the survival functions as outcomes of the control and treatment neural networks which consists of a set of neural kernels with shared parameters. The neural kernels are more flexible and can accurately model a complex location structure of feature vectors. Various numerical simulation experiments illustrate BENK and compare it with the well-known T-learner, S-learner and X-learner for several types of the control and treatment outcome functions based on the Cox models, the random survival forest and the Nadaraya-Watson regression with Gaussian kernels. The code of proposed algorithms implementing BENK is available in https://github.com/Stasychbr/BENK.

Machine Learning Techniques (like Regression, Classification, Clustering, Anomaly detection, etc.) are used to build the training data or a mathematical model using certain algorithms based upon the computations statistic to make prediction without the need of programming, as these techniques are influential in making the system futuristic, models and promotes automation of things with reduced cost and manpower. There are a few methods that are influential in promoting the systems to automatically learn and improve as per the experience. But they fall under various categories or types like Supervised Learning, Unsupervised Learning, Reinforcement Learning, Representation Learning, etc. Below are the techniques which fall under Machine Learning: Regression algorithms are mostly used to make predictions on numbers i.e when the output is a real or continuous value. As it falls under Supervised Learning, it works with trained data to predict new test data. For example, age can be a continuous value as it increases with time.

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient specific finite element analysis (FEA) computes the force (fracture load) to break the proximal femur in a particular loading condition. It provides different structural information about the proximal femur that can influence a subject overall fracture risk. To obtain a more robust measure of fracture risk, we used principal component analysis (PCA) to develop a global FEA computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies to failure in four loading conditions (single-limb stance and impact from a fall onto the posterior, posterolateral, and lateral aspects of the greater trochanter) of 110 hip fracture subjects and 235 age and sex matched control subjects from the AGES-Reykjavik study. We found that the first PC (PC1) of the FE parameters was the only significant predictor of hip fracture. Using a logistic regression model, we determined if prediction performance for hip fracture using PC1 differed from that using FE parameters combined by stratified random resampling with respect to hip fracture status. The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects. The AUC of PC1 and AUC of the FE parameters combined were not significantly different than that in the female subjects or in all subjects

Machine learning models are incorporated in different fields and disciplines in which some of them require a high level of accountability and transparency, for example, the healthcare sector. With the General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. A widely used category of explanation techniques attempts to explain models’ predictions by quantifying the importance score of each input feature. However, summarizing such scores to provide human-interpretable explanations is challenging. Another category of explanation techniques focuses on learning a domain representation in terms of high-level human-understandable concepts and then utilizing them to explain predictions. These explanations are hampered by how concepts are constructed, which is not intrinsically interpretable. To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features. CLEF maps the raw input features to high-level intuitive concepts and then decompose the evidence of prediction of the instance being explained into concepts. In addition, the proposed framework generates counterfactual explanations, suggesting the minimum changes in the instance’s concept based explanation that will lead to a different prediction. We demonstrate with simulated user feedback on predicting the risk of mortality. Such direct feedback is more effective than other techniques, that rely on hand-labelled or automatically extracted concepts, in learning concepts that align with ground truth concept definitions.

With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning centralized machine learning into a distributed one. However, privacy remains an unsolved problem in distributed machine learning. Multi-key homomorphic encryption over torus (MKTFHE) is one of the suitable candidates to solve the problem. However, there may be security risks in the decryption of MKTFHE and the most recent result about MKFHE only supports the Boolean operation and linear operation. So, MKTFHE cannot compute the non-linear function like Sigmoid directly and it is still hard to perform common machine learning such as logistic regression and neural networks in high performance. This paper first introduces secret sharing to propose a new distributed decryption protocol for MKTFHE, then designs an MKTFHE-friendly activation function, and finally utilizes them to implement logistic regression and neural network training in MKTFHE. We prove the correctness and security of our decryption protocol and compare the efficiency and accuracy between using Taylor polynomials of Sigmoid and our proposed function as an activation function. The experiments show that the efficiency of our function is 10 times higher than using 7-order Taylor polynomials straightly and the accuracy of the training model is similar to that of using a high-order polynomial as an activation function scheme.

Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\"ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.