Risk-Sensitive Choices in Surviving Selection, Regression and Removal


Risk-Sensitive Choices in Surviving Selection, Regression and Removal – Learning to control (MVC) agents is often a challenging task. It is known that most methods of MVC, such as neural network models, have been highly ineffective in training MVC agents (e.g., adversarial training methods) or performing MVC training with real-world agents. In this paper, we propose a novel unsupervised model of MVC agents (NMS) by combining the best of both worlds (adaptive learning) and learning from experience (adaptive learning), and apply that model to a novel problem of MVC agents in the context of adversarial control tasks. A new dataset is developed for MVC agents, trained on a real MVC agent in the wild. We evaluate our model on a simulated dataset and show that our method outperforms a variety of previous supervised models to the best of our knowledge, including the state-of-the-art MVC agent.

We present a framework for extracting latent variables from kernel Hilbert spaces. The method is formulated as a deep learning-based inference approach to kernel Hilbert spaces. We propose a nonparametric algorithm for learning the similarity scores of latent variables, which is evaluated by means of pairwise similarities. We show that this method, which is based on a simple formulation of the underlying kernel Hilbert space, can effectively extract a latent variable with high similarity while keeping a small but significant number of unlabeled examples, even when only a few examples are available on the machine. Finally, we demonstrate the effectiveness of our method by combining the latent variable discovery and learning with the training on multiple test data sets.

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Risk-Sensitive Choices in Surviving Selection, Regression and Removal

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  • Using a Gaussian Process Model and ABA Training to Improve Decision Forest Performance

    Scalable Kernel-Leibler Cosine Similarity PathWe present a framework for extracting latent variables from kernel Hilbert spaces. The method is formulated as a deep learning-based inference approach to kernel Hilbert spaces. We propose a nonparametric algorithm for learning the similarity scores of latent variables, which is evaluated by means of pairwise similarities. We show that this method, which is based on a simple formulation of the underlying kernel Hilbert space, can effectively extract a latent variable with high similarity while keeping a small but significant number of unlabeled examples, even when only a few examples are available on the machine. Finally, we demonstrate the effectiveness of our method by combining the latent variable discovery and learning with the training on multiple test data sets.


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