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+https://arxiv.org/pdf/1703.04529.pdf Task-based End-to-end Model Learning
+As machine learning techniques have become
+more ubiquitous, it has become common to see
+machine learning prediction algorithms operating
+within some larger process. However, the
+criteria by which we train machine learning algorithms
+often differ from the ultimate criteria
+on which we evaluate them. This paper proposes
+an end-to-end approach for learning probabilistic
+machine learning models within the context
+of stochastic programming, in a manner that directly
+captures the ultimate task-based objective
+for which they will be used. We then present
+two experimental evaluations of the proposed approach,
+one as applied to a generic inventory
+stock problem and the second to a real-world
+electrical grid scheduling task. In both cases,
+we show that the proposed approach can outperform
+both a traditional modeling approach and a
+purely black-box policy optimization approach.
+https://arxiv.org/abs/1611.03824 Learning to Learn for Global Optimization of Black Box Functions
+We present a learning to learn approach for training recurrent neural networks to perform black-box global optimization. In the meta-learning phase we use a large set of smooth target functions to learn a recurrent neural network (RNN) optimizer, which is either a long-short term memory network or a differentiable neural computer. After learning, the RNN can be applied to learn policies in reinforcement learning, as well as other black-box learning tasks, including continuous correlated bandits and experimental design. We compare this approach to Bayesian optimization, with emphasis on the issues of computation speed, horizon length, and exploration-exploitation trade-offs.
+https://arxiv.org/abs/1606.03152 Policy Networks with Two-Stage Training for Dialogue Systems
+First, we show that, on summary state and action spaces, deep Reinforcement Learning (RL) outperforms Gaussian Processes methods.
+We show that a deep RL method based on an actor-critic architecture can exploit a small amount of data very efficiently. Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL.