Noisy labels are inevitable in large real-world datasets.

We give superpolynomial statistical query (SQ) lower bounds for learning twohidden-layer ReLU networks with respect to Gaussian inputs in the standard (noise-free)model.

Scikit - learn: Machinelearninginpython.the Journalofmachine Learningresearch, 12: 2825-2830, 2011.

In reinforcement learning, robust policies for high-stakes decision-making problems with limited data are usually computed by optimizing the percentile criterion .

In contrast, linear RNNs have low computational complexity and are suitable for long-range modeling.