5 citations · 7 across the 6 of their papers we have counts for
6 papers
Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
Zhining Liu, Ze Yang, Xiao Lin +6
Time-series forecasting plays a critical role in many real-world applications. Although increasingly powerful models have been developed and achieved superior results on benchmark…
AIM: Attributing, Interpreting, Mitigating Data Unfairness
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +3
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals. Existing fair machine learning (FairML) research has pre…
Neural Active Learning Beyond Bandits
Yikun Ban, Ishika Agarwal, Ziwei Wu +4
We study both stream-based and pool-based active learning with neural network approximations. A recent line of works proposed bandit-based approaches that transformed active learni…
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
Yue Zhou, Yada Zhu, Diego Antognini +2
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface…
Fairness-aware Multi-view Clustering
Lecheng Zheng, Yada Zhu, Jingrui He
In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), t…
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Zixuan Yuan, Yada Zhu, Wei Zhang +3
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in…