3 citations · 11 across the 19 of their papers we have counts for
20 papers
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…
Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling
Yixuan Zhang, Jinhao Sheng, Wenxin Zhang +2
Although artificial neural networks are often described as brain-inspired, their representations typically rely on continuous activations, such as the continuous latent variables i…
Byte-token Enhanced Language Models for Temporal Point Processes Analysis
Quyu Kong, Yixuan Zhang, Yang Liu +3
Temporal Point Processes (TPPs) have been widely used for modeling event sequences on the Web, such as user reviews, social media posts, and online transactions. However, tradition…
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…
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
Junliang Lyu, Yixuan Zhang, Xiaoling Lu +1
This work addresses a key limitation in current federated learning approaches, which predominantly focus on homogeneous tasks, neglecting the task diversity on local devices. We pr…
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…
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng +4
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding…
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…
Hierarchical Optimization-Derived Learning
Risheng Liu, Xuan Liu, Shangzhi Zeng +2
In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been pr…
Characterizing and Detecting WebAssembly Runtime Bugs
Yixuan Zhang, Shangtong Cao, Haoyu Wang +6
WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and deskto…
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.…
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Risheng Liu, Xuan Liu, Shangzhi Zeng +2
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, p…
Value-Function-based Sequential Minimization for Bi-level Optimization
Risheng Liu, Xuan Liu, Shangzhi Zeng +2
Gradient-based Bi-Level Optimization (BLO) methods have been widely applied to handle modern learning tasks. However, most existing strategies are theoretically designed based on r…
Bias-Tolerant Fair Classification
Yixuan Zhang, Feng Zhou, Zhidong Li +2
The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decisi…
Nonlinear Hawkes Processes in Time-Varying System
Feng Zhou, Quyu Kong, Yixuan Zhang +2
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of…
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Feng Zhou, Yixuan Zhang, Jun Zhu
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities. Although utilized in many real applications,…