Search Results for author: Xuanhui Wang

Found 13 papers, 4 papers with code

Rank4Class: A Ranking Formulation for Multiclass Classification

no code implementations17 Dec 2021 Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We further demonstrate that the dominant neural MCC architecture can be formulated as a neural ranking framework with a specific set of design choices.

Classification Image Classification +2

Improving Neural Ranking via Lossless Knowledge Distillation

no code implementations30 Sep 2021 Zhen Qin, Le Yan, Yi Tay, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We explore a novel perspective of knowledge distillation (KD) for learning to rank (LTR), and introduce Self-Distilled neural Rankers (SDR), where student rankers are parameterized identically to their teachers.

Knowledge Distillation Learning-To-Rank

Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank

no code implementations29 Sep 2021 Nan Wang, Zhen Qin, Le Yan, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork

We further demonstrate that the most popular MCC architecture in deep learning can be mathematically formulated as a LTR pipeline equivalently, with a specific set of choices in terms of ranking model architecture and loss function.

Classification Image Classification +3

Distilling Interpretable Models into Human-Readable Code

1 code implementation21 Jan 2021 Walker Ravina, Ethan Sterling, Olexiy Oryeshko, Nathan Bell, Honglei Zhuang, Xuanhui Wang, Yonghui Wu, Alexander Grushetsky

The goal of model distillation is to faithfully transfer teacher model knowledge to a model which is faster, more generalizable, more interpretable, or possesses other desirable characteristics.

Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees

no code implementations ICLR 2021 Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets.

Learning-To-Rank

Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction

no code implementations18 May 2020 Nan Wang, Zhen Qin, Xuanhui Wang, Hongning Wang

Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback.

Learning-To-Rank

Learning-to-Rank with BERT in TF-Ranking

no code implementations17 Apr 2020 Shuguang Han, Xuanhui Wang, Mike Bendersky, Marc Najork

This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.

Document Ranking Learning-To-Rank +2

Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking

no code implementations21 Oct 2019 Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork

It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework.

Information Retrieval Learning-To-Rank

Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

2 code implementations11 Nov 2018 Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork

To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.

Learning-To-Rank

Multi-Faceted Ranking of News Articles using Post-Read Actions

no code implementations3 May 2012 Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang

Our findings show that it is possible to incorporate post-read signals that are commonly available on online news sites to improve quality of recommendations.

Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

4 code implementations31 Mar 2010 Lihong Li, Wei Chu, John Langford, Xuanhui Wang

\emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature.

News Recommendation Recommendation Systems

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