8 accepted papers
Ensuring robust model performance in diverse real-world scenarios requires addressing generalizability across domains with covariate shifts. However, no formal procedure exists for statistically evaluating generalizability in machine learning algorithms. Existing methods often rely on arbitrary prox
Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexity in causal benchmark datasets, which often fail to reproduce intricate real-world data patterns. In this paper we intr…
Predictive black-box models can exhibit high-accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide confidence for decision-making through increased transparency. However, existing XAI methods are not tailored towards m…
Directed acyclic graph models with hidden variables have been much studied, particularly in view of their computational efficiency and connection with causal methods. In this paper we provide the circumstances under which it is possible for two variables to be identically equal, while all other obse…
The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by columns of a known matrix. To develop the test, an ad…
The problem of detecting a subspace signal in the presence of subspace interference and contaminated Gaussian noise with unknown variance is investigated. The target signal is assumed to lie in a subspace spanned by the columns of a known matrix. To develop the test, the same steps used in the gener…
Adaptive subspace detectors (ASD) generalize matched subspace detectors (MSD) by accounting for possible correlation. Both ASD and MSD are derived using the generalized likelihood ratio test (GLRT). While MSD assumes there is no correlation between observations, ASD estimates a sample covariance mat…
In distributed tracking, communication bandwidth is one of the most expensive resources when we require to send measurements to other locations for processing. Furthermore, bandwidth requirement increases when tracking in clutter is considered due to transmission of target as well as clutter measure…