29 citations · 41 across the 22 of their papers we have counts for
11 papers · 1 filter
Causal Modeling of Selection in Evolution
Haoyue Dai, Zeyu Tang, Peter Spirtes +1
Understanding potential selection in data is crucial for causal discovery; we argue that "selection" in common narratives takes two forms, which we term static and evolutionary sel…
Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
Fan Feng, Selena Ge, Minghao Fu +6
Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these approaches often overlook latent f…
Should Bias be Eliminated? A General Framework to Use Bias for OOD Generalization
Yan Li, Yunlong Deng, Zijian Li +4
Most approaches to out-of-distribution (OOD) generalization learn domain-invariant representations by discarding contextual bias. In this paper, we raise a critical question: Shoul…
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs
Xiangchen Song, Aashiq Muhamed, Yujia Zheng +5
Sparse Autoencoders (SAEs) are a prominent tool in mechanistic interpretability (MI) for decomposing neural network activations into interpretable features. However, the aspiration…
When Selection Meets Intervention: Additional Complexities in Causal Discovery
Haoyue Dai, Ignavier Ng, Jianle Sun +5
We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug…
Detecting and Identifying Selection Structure in Sequential Data
Yujia Zheng, Zeyu Tang, Yiwen Qiu +2
We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. Since this selection process often disto…
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…
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
Zeyu Tang, Yatong Chen, Yang Liu +1
The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed…
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
Zeyu Tang, Jiji Zhang, Kun Zhang
Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections a…
Attainability and Optimality: The Equalized Odds Fairness Revisited
Zeyu Tang, Kun Zhang
Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have…
Model Transferability With Responsive Decision Subjects
Yatong Chen, Zeyu Tang, Kun Zhang +1
Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it?…