Learning Optimal Tree Models under Beam Search
Jul 12, 2020
Sprecher:innen
Über
Retrieving relevant targets from an extremely large target set under computation and time limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves in a tree hierarchy and associate tree nodes with trainable node-wise scorers, have attracted a lot of interests in tackling this challenge due to its logarithmic computational complexity in both training and testing. Tree-based deep models (TDMs) and probabilistic label trees (PLTs) are two kinds of representative tree models. Though achieving many practical successes, existing tree models still suffer from training-testing discrepancy: in testing they usually leverage beam search to retrieve targets from the tree, which is not considered in the training loss function. As a result, even the optimal node-wise scorers with respect to the training loss can lead to suboptimal retrieval results when they are used in testing to retrieve targets via beam search. In this paper, we take a first step towards understanding the discrepancy by developing the definition of Bayes optimality and calibration under beam search as general analyzing tools, and prove that neither TDMs nor PLTs are Bayes optimal under beam search. To eliminating the discrepancy, we propose a novel training loss function with a beam search based subsampling method for training Bayes optimal tree models under beam search. Experiments on both synthetic and real data verify our analysis and demonstrate the superiority of our methods.Retrieving relevant targets from an extremely large target set under computation and time limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves in a tree hierarchy and associate tree nodes with trainable node-wise scorers, have attracted a lot of interests in tackling this challenge due to its logarithmic computational complexity in both training and testing. Tree-based deep models (TDMs) and probabilistic label trees (P…
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Über 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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