12 citations · 16 across the 20 of their papers we have counts for
7 papers · 1 filter
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
Yiqiao Liao, Parinaz Naghizadeh
Existing learning-based influence maximization frameworks rely heavily on complex neural architectures and continuous optimization over seed representations. We challenge this para…
Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing
Yifan Yang, Yang Liu, Parinaz Naghizadeh
The performance of algorithmic decision rules is largely dependent on the quality of training datasets available to them. Biases in these datasets can raise economic and ethical co…
The Double-Edged Sword of Behavioral Responses in Strategic Classification: Theory and User Studies
Raman Ebrahimi, Kristen Vaccaro, Parinaz Naghizadeh
When humans are subject to an algorithmic decision system, they can strategically adjust their behavior accordingly (``game'' the system). While a growing line of literature on str…
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering
Yifan Yang, Ali Payani, Parinaz Naghizadeh
Federated Learning (FL) has been a pivotal paradigm for collaborative training of machine learning models across distributed datasets. In heterogeneous settings, it has been observ…
Generalization Error Bounds for Learning under Censored Feedback
Yifan Yang, Ali Payani, Parinaz Naghizadeh
Generalization error bounds from learning theory provide statistical guarantees on how well an algorithm will perform on previously unseen data. In this paper, we characterize the…
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability
Md Ferdous Alam, Parinaz Naghizadeh, David Hoelzle
Reinforcement learning (RL) enables sequential decision-making in complex and high-dimensional environments through interaction with the environment. In most real-world application…
Adaptive Data Debiasing through Bounded Exploration
Yifan Yang, Yang Liu, Parinaz Naghizadeh
Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propos…