35 citations · 91 across the 20 of their papers we have counts for
10 papers · 1 filter
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
Jialu Wang, Heinrich Peters, Asad A. Butt +6
Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training method…
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
Usman Gohar, Zeyu Tang, Jialu Wang +4
The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works ha…
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Jinlong Pang, Jialu Wang, Zhaowei Zhu +3
The pursuit of fairness in machine learning (ML), ensuring that the models do not exhibit biases toward protected demographic groups, typically results in a compromise scenario. Th…
Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Zhaowei Zhu, Jialu Wang, Hao Cheng +1
Language models have shown promise in various tasks but can be affected by undesired data during training, fine-tuning, or alignment. For example, if some unsafe conversations are…
Understanding Instance-Level Impact of Fairness Constraints
Jialu Wang, Xin Eric Wang, Yang Liu
A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias. Their impacts have been largely evaluated for different groups of p…
Fairness Transferability Subject to Bounded Distribution Shift
Yatong Chen, Reilly Raab, Jialu Wang +1
Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? I…
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Zhaowei Zhu, Jialu Wang, Yang Liu
The label noise transition matrix, denoting the transition probabilities from clean labels to noisy labels, is crucial for designing statistically robust solutions. Existing estima…
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
Yang Liu, Jialu Wang
In this paper, we answer the question of when inserting label noise (less informative labels) can instead return us more accurate and fair models. We are primarily inspired by thre…
Fair Classification with Group-Dependent Label Noise
Jialu Wang, Yang Liu, Caleb Levy
This work examines how to train fair classifiers in settings where training labels are corrupted with random noise, and where the error rates of corruption depend both on the label…
Linear Classifiers that Encourage Constructive Adaptation
Yatong Chen, Jialu Wang, Yang Liu
Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss i…