In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Input: Consider Input: Synthesizing user-intended programs from a small number of input-output exam-
 ples is a challenging problem with several important applications like spreadsheet
 manipulation, data wrangling and code refactoring. Existing synthesis systems
 either completely rely on deductive logic techniques that are extensively hand-
 engineered or on purely statistical models that need massive amounts of data, and in
 general fail to provide real-time synthesis on challenging benchmarks. In this work,
 we propose Neural Guided Deductive Search (NGDS), a hybrid synthesis technique
 that combines the best of both symbolic logic techniques and statistical models.
 Thus, it produces programs that satisfy the provided specifications by construction
 and generalize well on unseen examples, similar to data-driven systems. Our
 technique effectively utilizes the deductive search framework to reduce the learning
 problem of the neural component to a simple supervised learning setup. Further,
 this allows us to both train on sparingly available real-world data and still leverage
 powerful recurrent neural network encoders. We demonstrate the effectiveness
 of our method by evaluating on real-world customer scenarios by synthesizing
 accurate programs with up to 12× speed-up compared to state-of-the-art systems.

Output: We integrate symbolic (deductive) and statistical (neural-based) methods to enable real-time program synthesis with almost perfect generalization from 1 input-output example.


Input: Consider Input: We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode. We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function. RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation. RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings.

Output: We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.


Input: Consider Input: This paper addresses the problem of representing a system's belief using multi-variate normal distributions (MND) where the underlying model is based on a deep neural network (DNN). The major challenge with DNNs is the computational complexity that is needed to obtain model uncertainty using MNDs. To achieve a scalable method, we propose a novel approach that expresses the parameter posterior in sparse information form. Our inference algorithm is based on a novel Laplace Approximation scheme, which involves a diagonal correction of the Kronecker-factored eigenbasis. As this makes the inversion of the information matrix intractable - an operation that is required for full Bayesian analysis, we devise a low-rank   approximation of this eigenbasis and a memory-efficient sampling scheme. We provide both a theoretical analysis and an empirical evaluation on various benchmark data sets, showing the superiority of our approach over existing methods.


Output: An approximate inference algorithm for deep learning