The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …

Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …

The existing literature on deep learning for tabular data proposes a wide range of novel
architectures and reports competitive results on various datasets. However, the proposed …

Pretrained language models such as BERT have been shown to be exceptionally effective
for text ranking. However, there are limited studies on how to leverage more powerful …

We consider the large-scale query-document retrieval problem: given a query (eg, a
question), return the set of relevant documents (eg, paragraphs containing the answer) from …

Tree boosting is a highly effective and widely used machine learning method. In this paper,
we describe a scalable end-to-end tree boosting system called XGBoost, which is used …

Current benchmark reports of classification algorithms generally concern common classifiers
and their variants but do not include many algorithms that have been introduced in recent …

Abstract Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to
the users, but they are oblivious to their impact on the ranked items. However, there has …

Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …

Learning to rank has been intensively studied and widely applied in information retrieval.
Typically, a global ranking function is learned from a set of labeled data, which can achieve …