In the regression problem, we consider the problem of estimating the variance function by the means of aggregation methods. We focus on two particular aggregation setting: Model Selection aggregation (MS) and Convex aggregation (C) where the goal is to select the best candidate and to build the best convex combination of candidates respectively among a collection of candidates. In both cases, the construction of the estimator relies on a two-step procedure and requires two independent samples. The first step exploits the first sample to build the candidate estimators for the variance function by the residual-based method and then the second dataset is used to perform the aggregation step. We show the consistency of the proposed method with respect to the L 2error both for MS and C aggregations. We evaluate the performance of these two methods in the heteroscedastic model and illustrate their interest in the regression problem with reject option.

The AI and Machine Learning industry is booming like never before. As of 2021, the increase in AI usage across businesses will create $2.9 trillion of business value. AI has automated many industries across the globe and changed the way they operate. Most large companies incorporate AI to maximize productivity in their workflow, and industries like marketing and healthcare have undergone a paradigm shift due to the consolidation of AI. Due to this, there has been an increasing demand in the past few years for AI professionals. There has almost been a 100% increase in AI and machine learning-related job postings from 2015 to 2018. This number has grown since and is projected to rise in 2021. If you are looking to break into the machine learning industry, the good news is that there is no shortage of jobs available. Companies need a talented workforce that is capable of pioneering the shift to machine learning.

Many causal inference approaches have focused on identifying an individual's outcome change due to a potential treatment, or the individual treatment effect (ITE), from observational studies. Rather than only estimating the ITE, we propose Collaborating Causal Networks (CCN) to estimate the full potential outcome distributions. This modification facilitates estimating the utility of each treatment and allows for individual variation in utility functions (e.g., variability in risk tolerance). We show that CCN learns distributions that asymptotically capture the correct potential outcome distributions under standard causal inference assumptions. Furthermore, we develop a new adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational studies. We evaluate CCN by extensive empirical experiments and demonstrate improved distribution estimates compared to existing Bayesian and Generative Adversarial Network-based methods. Additionally, CCN empirically improves decisions over a variety of utility functions.

This PhD thesis contains several contributions to the field of statistical causal modeling. Statistical causal models are statistical models embedded with causal assumptions that allow for the inference and reasoning about the behavior of stochastic systems affected by external manipulation (interventions). This thesis contributes to the research areas concerning the estimation of causal effects, causal structure learning, and distributionally robust (out-of-distribution generalizing) prediction methods. We present novel and consistent linear and non-linear causal effects estimators in instrumental variable settings that employ data-dependent mean squared prediction error regularization. Our proposed estimators show, in certain settings, mean squared error improvements compared to both canonical and state-of-the-art estimators. We show that recent research on distributionally robust prediction methods has connections to well-studied estimators from econometrics. This connection leads us to prove that general K-class estimators possess distributional robustness properties. We, furthermore, propose a general framework for distributional robustness with respect to intervention-induced distributions. In this framework, we derive sufficient conditions for the identifiability of distributionally robust prediction methods and present impossibility results that show the necessity of several of these conditions. We present a new structure learning method applicable in additive noise models with directed trees as causal graphs. We prove consistency in a vanishing identifiability setup and provide a method for testing substructure hypotheses with asymptotic family-wise error control that remains valid post-selection. Finally, we present heuristic ideas for learning summary graphs of nonlinear time-series models.

In a traditional paradigm of machine learning, we often work in the offline learning fashion where we start with data preprocessing and end with data modelling with an algorithm to satisfy the requirements. This becomes a storage-dependent and time-consuming process. To overcome this, we can use streaming data for predictive analysis or any other modelling process. We don't need to store the data before modelling it. This can be accomplished by stream learning and online learning.

This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning. Machine Learning is the study of computer algorithms that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning is actively being used today, perhaps in many more places than one world expects.

Presently, nearly all manual tasks are being automated. Machine learning algorithms are changing the definition of manual. It is very evident that machine learning is one of the hottest trends in the tech industry and is incredibly powerful to make predictions, and calculated suggestions based on large amounts of data. Machine learning engineers should be thorough with the routine algorithms to understand ML operations and execute advanced techniques. Here are the top 10 machine learning algorithms every engineer should know.

Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.

While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improve accuracy of prediction and quality of care. Predicting ED may help aid shared decision making by making the advantages and disadvantages of certain treatments clear, so that a tailored treatment for an individual patient can be chosen. This study aimed to predict ED at 1-year and 2-year post-diagnosis based on patient demographics, clinical data and patient-reported outcomes (PROMs) measured at diagnosis.

This transformation of food delivery businesses to online platforms has gained high attention in recent years. This due to the availability of customizing ordering experiences, easy payment methods, fast delivery, and others. The competition between online food delivery providers has intensified to attain a wider range of customers. Hence, they should have a better understanding of their customers' needs and predict their purchasing decisions. Machine learning has a significant impact on companies' bottom line. They are used to construct models and strategies in industries that rely on big data and need a system to evaluate it fast and effectively. Predictive modeling is a type of machine learning that uses various regression algorithms, analytics, and statistics to estimate the probability of an occurrence. The incorporation of predictive models helps online food delivery providers to understand their customers. In this study, a dataset collected from 388 consumers in Bangalore, India was provided to predict their purchasing decisions. Four prediction models are considered: CART and C4.5 decision trees, random forest, and rule-based classifiers, and their accuracies in providing the correct class label are evaluated. The findings show that all models perform similarly, but the C4.5 outperforms them all with an accuracy of 91.67%.