Khashayar Khosravi

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Recurrent neural networks have been very successful at predicting sequences of words in tasks such as language modeling. However, all such models are based on the conventional classification framework, where model is trained against one-hot targets, and each word is represented both as an input and as an output in isolation. This causes inefficiencies in(More)
We present two improvements to the well-known Recurrent Neural Network Language Models(RNNLM). First, we use the word embedding matrix to project the RNN output onto the output space and already achieve a large reduction in the number of free parameters while still improving performance. Second, instead of merely minimizing the standard cross entropy loss(More)
<lb>We provide a unifying view of statistical information measures, multi-class classification<lb>problems, multi-way Bayesian hypothesis testing, and loss functions, elaborating equivalence<lb>results between all of these objects. In particular, we consider a particular generalization of<lb>f -divergences to multiple distributions, and we show that there(More)
The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy policies that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy policies are desirable in many practical settings where exploration may be(More)
The contextual bandit literature has traditionally focused on algorithms that address the explorationexploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in many practical settings where exploration may be(More)