3 citations · 3 across the 6 of their papers we have counts for
6 papers
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
Wenqin Liu, Weizhi Quan, Aoqi Zuo +5
Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains…
FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning
Yuxi Sun, Aoqi Zuo, Haotian Xie +3
Chain-of-Thought (CoT) prompting has improved LLM reasoning, but models often generate explanations that appear coherent while containing unfaithful intermediate steps. Existing se…
Observationally Informed Adaptive Causal Experimental Design
Erdun Gao, Liang Zhang, Jake Fawkes +5
Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is ut…
CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention
Yuxi Sun, Aoqi Zuo, Wei Gao +1
Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to \textit{abstain} when faced with knowledge gaps is a promising strategy to re…
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
Aoqi Zuo, Yiqing Li, Susan Wei +1
Fair machine learning aims to prevent discrimination against individuals or sub-populations based on sensitive attributes such as gender and race. In recent years, causal inference…
Counterfactual Fairness with Partially Known Causal Graph
Aoqi Zuo, Susan Wei, Tongliang Liu +3
Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair mach…