Artur Dubrawski

Learn More
In this paper we describe a new method for automated tuning of hyper-parameters of supervised learning systems. It uses memory-based learning principles, follows certain ideas of experimental design and employs an alternative approach to resampling called stechastic validation. The described method allows not only for an efficient search through a decision(More)
In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a generally transferable structure of the language and propose a new method to learn this structure using an appropriately chosen latent variable model. This semantic correlation contains(More)
In many applications, classification systems often require human intervention in the loop. In such cases the decision process must be transparent and comprehensible, simultaneously requiring minimal assumptions on the underlying data distributions. To tackle this problem, we formulate an axis-aligned subspace-finding task under the assumption that query(More)
In this paper we present a novel scheme for unstructured audio scene classification that possesses three highly desirable and powerful features: autonomy, scalability, and robustness. Our scheme is based on our recently introduced machine learning algorithm called Simultaneous Temporal And Contextual Splitting (STACS) that discovers the appropriate number(More)
Data driven decision support systems often benefit from human participation to validate outcomes produced by automated procedures. Perceived utility hinges on the system's ability to learn transparent, comprehensible models from data. We introduce and formalize Informative Projection Recovery: the problem of extracting a set of low-dimensional projections(More)
This report introduces a data structure called T-Cube designed to dramatically improve response time to ad-hoc time series queries against large datasets. We have tested T-Cube on both synthetic and real world data (emergency room patient visits, pharmacy sales) containing millions of records. The results indicate that T-Cube responds to complex queries(More)
A variety of learning problems in robotics, computer vision and other areas of artificial intelligence can be construed as problems of learning statistical models for dynamical systems from sequential observations. Good dynamical system models allow us to represent and predict observations in these systems, which in turn enables applications such as(More)
OBJECTIVE The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. DESIGN Observational cohort study. SETTING Twenty-four-bed trauma step-down unit. PATIENTS Two thousand one hundred fifty-three patients. INTERVENTION Noninvasive(More)