1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
SPRINT: Stochastic Performative Prediction With Variance Reduction
Tian Xie, Ding Zhu, Jia Liu +2
Performative prediction (PP) is an algorithmic framework for optimizing machine learning (ML) models where the model's deployment affects the distribution of the data it is trained…
Post-processing for Fair Regression via Explainable SVD
Zhiqun Zuo, Ding Zhu, Mohammad Mahdi Khalili
This paper presents a post-processing algorithm for training fair neural network regression models that satisfy statistical parity, utilizing an explainable singular value decompos…
An Efficient Training Algorithm for Models with Block-wise Sparsity
Ding Zhu, Zhiqun Zuo, Mohammad Mahdi Khalili
Large-scale machine learning (ML) models are increasingly being used in critical domains like education, lending, recruitment, healthcare, criminal justice, etc. However, the train…
Neuroplasticity and Corruption in Model Mechanisms: A Case Study Of Indirect Object Identification
Vishnu Kabir Chhabra, Ding Zhu, Mohammad Mahdi Khalili
Previous research has shown that fine-tuning language models on general tasks enhance their underlying mechanisms. However, the impact of fine-tuning on poisoned data and the resul…