Equation 9. Therefore if we define a standard "policy" loss L This is the "soft" version of an analogous statement made for "hard" optimality first shown in [32]. This argument is the direct counterpart to Theorem 2 in [32]--which uses argmax instead of softmax. From this point onwards, the same strategy for Proposition 2 again applies, completing the proof. Environments used for experiments are from OpenAI gym [56]. Each environment is associated with a true reward function (unknown to all imitation algorithms).

In contrast to previous methods, we propose a new search strategy that does not require any repeated training of child models.

We are interested in computing the Lipschitz constant of neural networks with ReLU activations.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We develop and test two potential solutions: an alternate validation method ("sample validation") and a novel regularization method ("coordinated

Solving optimization problems is the key to decision making in many real-life analytics applications. However, the coefficients of the optimization problems are often uncertain and dependent on external factors, such as future demand or energy or stock prices. Machine learning (ML) models, especially neural networks, are increasingly being used to estimate these coefficients in a data-driven way.

The transition matrix, denoting the transition relationship from clean labels to noisy labels, is essential to build statistically consistent classifiers in label-noise learning.