Traditional time series forecasting methods applied to long time series and multivariate data
often ignore the importance of features and the causal relationships between predictors and …

Time-series forecasting is a fundamental problem associated with a wide range of
engineering, financial, and social applications. The challenge arises from the complexity …

Given a social issue that needs to be solved, decision-makers need to listen to the crowd
opinions and preferences. However, existing online voting systems with limited capabilities …

This paper addresses the cost-efficiency aspect of Reinforcement Learning from Human
Feedback (RLHF). RLHF leverages datasets of human preferences over outputs of LLMs to …

Given a set of ideas collected from crowds with regard to an open-ended question, how can
we organize and prioritize them in order to determine the preferred ones based on …

Identifying potentially high-performing students is crucial for universities aiming to enhance
educational outcomes, for companies seeking to recruit top talents early, and for advertising …

This paper addresses the challenges of aligning large language models (LLMs) with human
values via preference learning (PL), focusing on incomplete and corrupted data in …

Abstract The seminal Bradley-Terry model exhibits transitivity, that is, the property that the
probabilities of player A beating B and B beating C give the probability of A beating C, with …

In software testing, the quality of test cases is crucial, butmanual generation is time-
consuming. Various automatic test case generation methods exist, requiring careful …

Ranking is a ubiquitous problem setting that appears in many real-world applications. The
superior players or objects in the ranking list are oftentimes estimated from matchups and …