463 citations · 904 across the 24 of their papers we have counts for
24 papers
Learning to Rank when Grades Matter
Le Yan, Zhen Qin, Gil Shamir +3
Graded labels are ubiquitous in real-world learning-to-rank applications, especially in human rated relevance data. Traditional learning-to-rank techniques aim to optimize the rank…
RD-Suite: A Benchmark for Ranking Distillation
Zhen Qin, Rolf Jagerman, Rama Pasumarthi +6
The distillation of ranking models has become an important topic in both academia and industry. In recent years, several advanced methods have been proposed to tackle this problem,…
LibAUC: A Deep Learning Library for X-Risk Optimization
Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu +3
This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-…
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Zhen Qin, Rolf Jagerman, Kai Hui +9
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem. However, researc…
Query Expansion by Prompting Large Language Models
Rolf Jagerman, Honglei Zhuang, Zhen Qin +2
Query expansion is a widely used technique to improve the recall of search systems. In this paper, we propose an approach to query expansion that leverages the generative abilities…
Metric-agnostic Ranking Optimization
Qingyao Ai, Xuanhui Wang, Michael Bendersky
Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of rank…
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
Yunan Zhang, Le Yan, Zhen Qin +5
Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention rec…
Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian, Honglei Zhuang, Zhen Qin +7
Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning,…
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
Aijun Bai, Rolf Jagerman, Zhen Qin +6
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score…
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman +6
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful s…
Rank4Class: A Ranking Formulation for Multiclass Classification
Nan Wang, Zhen Qin, Le Yan +4
Multiclass classification (MCC) is a fundamental machine learning problem of classifying each instance into one of a predefined set of classes. In the deep learning era, extensive…
Improving Neural Ranking via Lossless Knowledge Distillation
Zhen Qin, Le Yan, Yi Tay +4
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…
Distilling Interpretable Models into Human-Readable Code
Walker Ravina, Ethan Sterling, Olexiy Oryeshko +5
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 ch…
Interpretable Learning-to-Rank with Generalized Additive Models
Honglei Zhuang, Xuanhui Wang, Michael Bendersky +7
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area. Recent progress on interpretable ranking models largely focuses on generating…
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
Nan Wang, Zhen Qin, Xuanhui Wang +1
Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback. Though theoretically sound in correcting the bi…
Learning-to-Rank with BERT in TF-Ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky +1
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…
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky +1
How to leverage cross-document interactions to improve ranking performance is an important topic in information retrieval (IR) research. However, this topic has not been well-studi…
Estimating Position Bias without Intrusive Interventions
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang +3
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counter…
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang +7
Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applicati…
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
Qingyao Ai, Xuanhui Wang, Sebastian Bruch +3
While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire…
Offline Comparison of Ranking Functions using Randomized Data
Aman Agarwal, Xuanhui Wang, Cheng Li +2
Ranking functions return ranked lists of items, and users often interact with these items. How to evaluate ranking functions using historical interaction logs, also known as off-po…
Multi-Faceted Ranking of News Articles using Post-Read Actions
Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang
Personalized article recommendation is important to improve user engagement on news sites. Existing work quantifies engagement primarily through click rates. We argue that quality…
Modeling Perceived Relevance for Tail Queries without Click-Through Data
Changsung Kang, Xiaotong Lin, Xuanhui Wang +2
Click-through data has been used in various ways in Web search such as estimating relevance between documents and queries. Since only search snippets are perceived by users before…
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Lihong Li, Wei Chu, John Langford +1
Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the e…