2 citations · 4 across the 10 of their papers we have counts for
10 papers
Efficient Temporal Point Processes via Monotone Alternating Splines
Cheng Wan, Quyu Kong, Feng Zhou
Temporal point processes (TPPs) have widespread applications across various domains. Compared to modeling the conditional intensity of a TPP, modeling its cumulative conditional in…
Score Matching for Estimating Finite Point Processes
Haoqun Cao, Yixuan Zhang, Feng Zhou
Score matching estimators have garnered significant attention in recent years because they eliminate the need to compute normalizing constants, thereby mitigating the computational…
Fair Bayesian Data Selection via Generalized Discrepancy Measures
Yixuan Zhang, Jiabin Luo, Zhenggang Wang +2
Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the mo…
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
Feng Zhou, Quyu Kong, Jie Qiao +3
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing h…
Navigating Towards Fairness with Data Selection
Yixuan Zhang, Zhidong Li, Yang Wang +3
Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fai…
Nonstationary Sparse Spectral Permanental Process
Zicheng Sun, Yixuan Zhang, Zenan Ling +2
Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel appr…
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
Yixuan Zhang, Feng Zhou
Fine-tuning pre-trained models is a widely employed technique in numerous real-world applications. However, fine-tuning these models on new tasks can lead to unfair outcomes. This…
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
Yixuan Zhang, Boyu Li, Zenan Ling +1
Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and ca…
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
Yixuan Zhang, Quyu Kong, Feng Zhou
In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP i…
De-biased Representation Learning for Fairness with Unreliable Labels
Yixuan Zhang, Feng Zhou, Zhidong Li +2
Removing bias while keeping all task-relevant information is challenging for fair representation learning methods since they would yield random or degenerate representations w.r.t.…