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.
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Question: We develop a worst-case analysis of aggregation of binary classifier ensembles in a transductive setting, for a broad class of losses including but not limited to all convex surrogates. The result is a family of parameter-free ensemble aggregation algorithms, which are as efficient as linear learning and prediction for convex risk minimization but work without any relaxations whatsoever on many nonconvex losses like the 0-1 loss. The prediction algorithms take a familiar form, applying "link functions" to a generalized notion of ensemble margin, but without the assumptions typically made in margin-based learning all this structure follows from a minimax interpretation of loss minimization.

Answer: Minimax Binary Classifier Aggregation with General Losses

Question: We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features. Parameter estimation is arranged to maximize accuracy and early confidence in this sequence. Our approach is simpler and better suited to NLP than other related cascade methods. We present experiments in left-to-right part-of-speech tagging, named entity recognition, and transition-based dependency parsing. On the typical benchmarking datasets we can preserve POS tagging accuracy above 97% and parsing LAS above 88.5% both with over a five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase in speed.

Answer: Learning Dynamic Feature Selection for Fast Sequential Prediction

Question: <lb>We show that any model trained by a stochastic gradient method with few iterations has<lb>vanishing generalization error. We prove this by showing the method is algorithmically stable<lb>in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex<lb>and continuous optimization. Our results apply to both convex and non-convex optimization<lb>under standard Lipschitz and smoothness assumptions.<lb>Applying our results to the convex case, we provide new explanations for why multiple<lb>epochs of stochastic gradient descent generalize well in practice. In the nonconvex case, we<lb>provide a new interpretation of common practices in neural networks, and provide a formal<lb>rationale for stability-promoting mechanisms in training large, deep models. Conceptually, our<lb>findings underscore the importance of reducing training time beyond its obvious benefit.

Answer:
Stability of stochastic gradient descent