5 papers
Causally Fair Node Classification on Non-IID Graph Data
Yucong Dai, Lu Zhang, Yaowei Hu +2
Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes, such as race and gender. Recent…
Back to Blackwell: Closing the Loop on Intransitivity in Multi-Objective Preference Fine-Tuning
Jiahao Zhang, Lujing Zhang, Keltin Grimes +3
A recurring challenge in preference fine-tuning (PFT) is handling (i.e., cyclic) preferences. Intransitive preferences often stem from either …
ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
Lujing Zhang, Daniel Hsu, Sivaraman Balakrishnan
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose ShakyPrepend, a method that leverages tools…
FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
Yucong Dai, Lu Zhang, Feng Luo +2
Training fair and unbiased machine learning models is crucial for high-stakes applications, yet it presents significant challenges. Effective bias mitigation requires deep expertis…
A Causal Lens for Learning Long-term Fair Policies
Jacob Lear, Lu Zhang
Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on imme…