In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Proposed methods for prediction interval estimation so far focus on cases where input variables are numerical. In datasets with solely nominal input variables, we observe records with the exact same input x, but different real valued outputs due to the inherent noise in the system. Existing prediction interval estimation methods do not use representations that can accurately model such inherent noise in the case of nominal inputs. We propose a new prediction interval estimation method tailored for this type of data, which is prevalent in biology and medicine. We call this method Distribution Adaptive Prediction Interval Estimation given Nominal inputs (DAPIEN) and has four main phases. First, we select a distribution function that can best represent the inherent noise of the system for all unique inputs. Then we infer the parameters θi (e.g. θi = [meani, variancei]) of the selected distribution function for all unique input vectors xi and generate a new corresponding training set using pairs of xi , θi. III). Then, we train a model to predict θ given a new xu. Finally, we calculate the prediction interval for a new sample using the inverse of the cumulative distribution function once the parameters θ is predicted by the trained model. We compared DAPIEN to the commonly used Bootstrap method on three synthetic datasets. Our results show that DAPIEN provides tighter prediction intervals while preserving the requested coverage when compared to Bootstrap. This work can facilitate broader usage of regression methods in medicine and biology where it is necessary to provide tight prediction intervals while preserving coverage when input variables are nominal.

A: DICTION INTERVAL ESTIMATION USING NOMINAL VARIABLES
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Q: The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set of temporally-extended actions, or options, each of which has its own subgoal. These subgoals are normally handcrafted for specific tasks. Here, though, we introduce a generic class of subgoals with broad applicability in the visual domain. Underlying our approach (in common with work using “auxiliary tasks”) is the hypothesis that the ability to control aspects of the environment is an inherently useful skill to have. We incorporate such subgoals in an end-to-end hierarchical reinforcement learning system and test two variants of our algorithm on a number of games from the Atari suite. We highlight the advantage of our approach in one of the hardest games – Montezuma’s revenge – for which the ability to handle sparse rewards is key. Our agent learns several times faster than the current state-of-the-art HRL agent in this game, reaching a similar level of performance.

A: Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
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Q: The representation of many common semantic phenomena requires structural properties beyond those commonly used for syntactic parsing. We discuss a set of structural properties required for broad-coverage semantic representation, and note that existing parsers support some of these properties, but not all. We propose two transition-based techniques for parsing such semantic structures: (1) applying conversion procedures to map them into related formalisms, and using existing state-of-the-art parsers on the converted representations; and (2) constructing a parser that directly supports the full set of properties. We experiment with UCCA-annotated corpora, the only ones with all these structural semantic properties. Results demonstrate the effectiveness of transition-based methods for the task.

A:
Broad-Coverage Semantic Parsing: A Transition-Based Approach
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