Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold. Moreover, feature learning in an unsupervised setup, without class labels and a suitable learning loss function, adds to the problem complexity. Further, the learned representation and subsequent predictions should not reflect discriminatory behavior towards certain sensitive groups or attributes. The proposed feature map should preserve maximum variations present in the data and needs to be fair with respect to the sensitive variables. We propose, in the first phase of our work, an efficient encoder-decoder framework to capture the mixed-domain information. The second phase of our work focuses on de-biasing the mixed space representations by adding relevant fairness constraints. This ensures minimal information loss between the representations before and after the fairness-preserving projections. Both the information content and the fairness aspect of the final representation learned has been validated through several metrics where it shows excellent performance. Our work (FairMixRep) addresses the problem of Mixed Space Fair Representation learning from an unsupervised perspective and learns a Universal representation that is timely, unique, and a novel research contribution.

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When building a machine learning model in real-life, it's almost rare that all the variables in the dataset are useful to build a model. Adding redundant variables reduces the generalization capability of the model and may also reduce the overall accuracy of a classifier. Furthermore adding more and more variables to a model increases the overall complexity of the model. As per the Law of Parsimony of'Occam's Razor', the best explanation to a problem is that which involves the fewest possible assumptions. Thus, feature selection becomes an indispensable part of building machine learning models.

Introduction: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Our aim was to evaluate the abilities of multiple artificial intelligence (AI) techniques to predict clinical outcomes based on variables collected at admission, during hospitalization, and at discharge. Methods: We used the multicenter North American Consortium for the Study of End-Stage Liver Disease (NACSELD) cohort of cirrhotic inpatients who are followed up through 90-days postdischarge for readmission and death. We used statistical methods to select variables that are significant for readmission and death and trained 3 AI models, including logistic regression (LR), kernel support vector machine (SVM), and random forest classifiers (RFC), to predict readmission and death. We used the area under the receiver operating characteristic curve (AUC) from 10-fold crossvalidation for evaluation to compare sexes.

Just like this KMeans clustering, our intention is to create clusters within our dataset, grouping related data so that we may determine different classes or groupings, to allow us to make predictions based on this information in a wide array of applications. However, in the areas in which KMeans fails, Hierarchy Clustering attempts to alleviate the burden somewhat with its several choices of novel techniques such as single-link clustering, or Ward Clustering, Hierarchy Clustering techniques chosen by the user, depending on the layout of their dataset. Hierarchy Clustering at the end of the day is just a regular clustering algorithm, with its advantages and disadvantages, and is by no means the successor of KMeans. Generally, Hierarchy Clustering works as follows; You start off with your dataset, which may be spaced out strange or have a weird layout with strange densities, where clusters are not easily differentiable by you, by all means, you honestly have no idea. That's alright, that usually is the case in real-world problems.

We describe the design of a reproducing kernel suitable for attributed graphs, in which the similarity between the two graphs is defined based on the neighborhood information of the graph nodes with the aid of a product graph formulation. We represent the proposed kernel as the weighted sum of two other kernels of which one is an R-convolution kernel that processes the attribute information of the graph and the other is an optimal assignment kernel that processes label information. They are formulated in such a way that the edges processed as part of the kernel computation have the same neighborhood properties and hence the kernel proposed makes a well-defined correspondence between regions processed in graphs. These concepts are also extended to the case of the shortest paths. We identified the state-of-the-art kernels that can be mapped to such a neighborhood preserving framework. We found that the kernel value of the argument graphs in each iteration of the Weisfeiler-Lehman color refinement algorithm can be obtained recursively from the product graph formulated in our method. By incorporating the proposed kernel on support vector machines we analyzed the real-world data sets and it has shown superior performance in comparison with that of the other state-of-the-art graph kernels.

Aim: One of the aims of the Observation Health Data Sciences and Informatics (OHDSI) initiative is population-level treatment effect estimation in large observational databases. Since treatment effects are well-known to vary across groups of patients with different baseline risk, we aimed to extend the OHDSI methods library with a framework for risk-based assessment of treatment effect heterogeneity. Materials and Methods: The proposed framework consists of five steps: 1) definition of the problem, i.e. the population, the treatment, the comparator and the outcome(s) of interest; 2) identification of relevant databases; 3) development of a prediction model for the outcome(s) of interest; 4) estimation of propensity scores within strata of predicted risk and estimation of relative and absolute treatment effect within strata of predicted risk; 5) evaluation and presentation of results. Results: We demonstrate our framework by evaluating heterogeneity of the effect of angiotensin-converting enzyme (ACE) inhibitors versus beta blockers on a set of 9 outcomes of interest across three observational databases. With increasing risk of acute myocardial infarction we observed increasing absolute benefits, i.e. from -0.03% to 0.54% in the lowest to highest risk groups. Cough-related absolute harms decreased from 4.1% to 2.6%. Conclusions: The proposed framework may be useful for the evaluation of heterogeneity of treatment effect on observational data that are mapped to the OMOP Common Data Model. The proof of concept study demonstrates its feasibility in large observational data. Further insights may arise by application to safety and effectiveness questions across the global data network.

Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations by converting these constraints into differentiable learning objectives. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process. We also present a promising case study showing the effectiveness of our approach in inducing event complexes on an external corpus.

We find, through empirical and theoretical drops are created equal, and being able to associate a particular results, that supervised Latent Dirichlet Allocation (sLDA) implemented move to an underlying reason may yield important insights about through Gibbs sampling in a stochastic EM algorithm will what comes next. This paper fits into a broader research agenda that often overfit returns to the detriment of the topic model. We obtain aims to first explain contemporaneous price moves, and only then to better out-of-sample performance through a random search of plain think about forecasting future ones. See [11] and references there LDA models. A branching procedure that reinforces effective topic for background on news and text analysis in financial economics.

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks. Unfortunately, HMC has limited use in large-data regimes and little work has explored suitable approaches that aim to preserve the entire Hamiltonian. In our work, we introduce a new symmetric integration scheme for split HMC that does not rely on stochastic gradients. We show that our new formulation is more efficient than previous approaches and is easy to implement with a single GPU. As a result, we are able to perform full HMC over common deep learning architectures using entire data sets. In addition, when we compare with stochastic gradient MCMC, we show that our method achieves better performance in both accuracy and uncertainty quantification. Our approach demonstrates HMC as a feasible option when considering inference schemes for large-scale machine learning problems.