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Yiqun Hu

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3 papers

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ICLR Conference 2023 Conference Paper

DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

Donghan Yu
Sheng Zhang 0029
Patrick Ng
Henghui Zhu
Alexander Hanbo Li
Jun Wang 0122
Yiqun Hu
William Yang Wang

Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either generate logical forms that can be executed over KBs to obtain final answers or predict answers directly. Empirical results show that the former often produces more accurate answers, but it suffers from non-execution issues due to potential syntactic and semantic errors in the generated logical forms. In this work, we propose a novel framework DecAF that jointly generates both logical forms and direct answers, and then combines the merits of them to get the final answers. Moreover, different from most of the previous methods, DecAF is based on simple free-text retrieval without relying on any entity linking tools --- this simplification eases its adaptation to different datasets. DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks, while getting competitive results on the ComplexWebQuestions benchmark.

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NeurIPS Conference 2022 Conference Paper

Learning Mixed Multinomial Logits with Provable Guarantees

Yiqun Hu
David Simchi-Levi
Zhenzhen Yan

A mixture of multinomial logits (MMNL) generalizes the single logit model, which is commonly used in predicting the probabilities of different outcomes. While extensive algorithms have been developed in the literature to learn MMNL models, theoretical results are limited. Built on the Frank-Wolfe (FW) method, we propose a new algorithm that learns both mixture weights and component-specific logit parameters with provable convergence guarantees for an arbitrary number of mixtures. Our algorithm utilizes historical choice data to generate a set of candidate choice probability vectors, each being close to the ground truth with a high probability. We further provide a sample complexity analysis to show that only a polynomial number of samples is required to secure the performance guarantee of our algorithm. Finally, we conduct simulation studies to evaluate the performance and demonstrate how to apply our algorithm to real-world applications.

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TIST Journal 2013 Journal Article

Learning image-to-class distance metric for image classification

Zhengxiang Wang
Yiqun Hu
Liang-Tien Chia

Image-To-Class (I2C) distance is a novel distance used for image classification and has successfully handled datasets with large intra-class variances. However, it uses Euclidean distance for measuring the distance between local features in different classes, which may not be the optimal distance metric in real image classification problems. In this article, we propose a distance metric learning method to improve the performance of I2C distance by learning per-class Mahalanobis metrics in a large margin framework. Our I2C distance is adaptive to different classes by combining with the learned metric for each class. These multiple per-class metrics are learned simultaneously by forming a convex optimization problem with the constraints that the I2C distance from each training image to its belonging class should be less than the distances to other classes by a large margin. A subgradient descent method is applied to efficiently solve this optimization problem. For efficiency and scalability to large-scale problems, we also show how to simplify the method to learn a diagonal matrix for each class. We show in experiments that our learned Mahalanobis I2C distance can significantly outperform the original Euclidean I2C distance as well as other distance metric learning methods in several prevalent image datasets, and our simplified diagonal matrices can preserve the performance but significantly speed up the metric learning procedure for large-scale datasets. We also show in experiment that our method is able to correct the class imbalance problem, which usually leads the NN-based methods toward classes containing more training images.

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