As the current literature contains multiple definitions of a dversarial risk and robustness, we start by giving a taxonomy for these definitions based on their direct goals; we identify one of them as the one guaranteeing miscla ssification by pushing the instances to the error region . We then study some classic algorithms for learning monotone conjunctions and compare their adversar ial robustness under different definitions by attacking the hypotheses using ins tances drawn from the uniform distribution. We observe that sometimes these defin itions lead to significantly different bounds. Thus, this study advocates for the use of the error-r egion definition, even though other definitions, in other contexts with context-dependent assumptions, may coincide with the error-region definition .

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data.

We give a smoothed analysis, showing that even when contexts may be chosen by an adversary, small perturbations of the adversary's choices suffice for the algorithm to achieve "no regret", perhaps (depending on the specifics of the setting) with a constant amount of initial training data.

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

On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM.

Machine understanding of complex images is a key goal of artificial intelligence.

NNS problem and should be used for the original data.

Our approach is to encourage an agent to reveal or hide their intentions using an information-theoretic regularizer.

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

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