Consistent Structured Prediction with Max-Min Margin Markov Networks

Jul 12, 2020



Max-margin methods for binary classification such as the support vector machine (SVM) have been extended to the structured prediction setting under the name of max-margin Markov networks (M^3N), or more generally structural SVMs. These methods are able to model interactions between output parts and incorporate a cost between labels. Unfortunately, these methods are inconsistent when the relationship between inputs and labels is far from deterministic. To overcome such limitations, in this paper we go beyond max-margin, defining the learning problem in terms of a “max-min” margin formulation. The resulting method, which we name max-min margin Markov networks (M^4N), provides a correction of the M^3N loss that is key to achieve consistency in the general case. In this paper, we prove consistency and finite sample generalization bounds for M^4N and provide an explicit algorithm to compute the estimator. The algorithm has strong statistical and computational guarantees: in a worst case scenario it achieves a generalization error of O(1/√(n)) for a total cost of O(n√(n)) marginalization-oracle calls, which have essentially the same cost as the max-oracle from M^3N. Experiments on multi-class classification and handwritten character recognition demonstrate the effectiveness of the proposed method over M^3N networks.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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