Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many
aspects of life. Search engines and recommender systems among others are used as …

We provide a theoretical framework for Reinforcement Learning with Human Feedback
(RLHF). We show that when the underlying true reward is linear, under both Bradley-Terry …

General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Systematically identifying functional peptides is difficult owing to the vast combinatorial
space of peptide sequences. Here we report a machine-learning pipeline that mines the …

A vital step towards the widespread adoption of neural retrieval models is their resource
efficiency throughout the training, indexing and query workflows. The neural IR community …

In various natural language processing tasks, passage retrieval and passage re-ranking are
two key procedures in finding and ranking relevant information. Since both the two …

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 …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …