In addition, since only the binary failure can be obtained, the number of latent constraints is also unknown.

Work done as a master's student at the University of Chicago. One way to compute this optimum is to use EM. We then get the following (see e.g., Sec 4.2.4 of Murphy [2022] In this section we discuss the datasets in more detail. B.1 Colored MNIST We show some sample images in Figure 1. We show some sample images in Figure 2. We list all the target attributes in Table 1.

Classification is a fundamental problem in machine learning, and considerable efforts have been recently devoted to the demanding long-tailed setting due to its prevalence in nature.

Our environment is riddled with sensory stimuli that are noisy, ambiguous, and often incomplete, necessitating organisms to handle uncertainty in their sensory observations.

's likelihood kernel that bounds the loss of information based on information-geometric

Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inherent uncertainty in the extrapolation of learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (ii) computationally expensive.