Yixuan Zhang - RankMe

27 papers A* 8A 1C 1Journal 15Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Byte-token Enhanced Language Models for Temporal Point Processes Analysis. WWW Quyu Kong, Yixuan Zhang, Yang Liu, Panrong Tong, Enqi Liu, Feng Zhou
2026	A*	`conf`	Fair Bayesian Data Selection via Generalized Discrepancy Measures. AAAI Yixuan Zhang, Jiabin Luo, Zhenggang Wang, Feng Zhou, Quyu Kong
2025	J	`jnl`	Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches. CoRR Feng Zhou, Quyu Kong, Yixuan Zhang
2025	J	`jnl`	Distilling the Unknown to Unveil Certainty. IEEE Trans. Pattern Anal. Mach. Intell. Zhi-Lin Zhao, Longbing Cao, Yixuan Zhang, Kun-Yu Lin, Wei-Shi Zheng
2025	J	`jnl`	Fair Bayesian Data Selection via Generalized Discrepancy Measures. CoRR Yixuan Zhang, Jiabin Luo, Zhenggang Wang, Feng Zhou, Quyu Kong
2025	A*	`conf`	Navigating Towards Fairness with Data Selection. AAAI Yixuan Zhang, Zhidong Li, Yang Wang, Fang Chen, Xuhui Fan, Feng Zhou
2025	—	`conf`	Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression. KDD (1) Junliang Lyu, Yixuan Zhang, Xiaoling Lu, Feng Zhou
2024	J	`jnl`	Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency. CoRR Yixuan Zhang, Feng Zhou
2024	A*	`conf`	Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures. ICML Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao
2024	J	`jnl`	Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures. CoRR Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao
2024	A*	`conf`	Mitigating Label Bias in Machine Learning: Fairness through Confident Learning. AAAI Yixuan Zhang, Boyu Li, Zenan Ling, Feng Zhou
2024	J	`jnl`	Navigating Towards Fairness with Data Selection. CoRR Yixuan Zhang, Zhidong Li, Yang Wang, Fang Chen, Xuhui Fan, Feng Zhou
2024	A*	`conf`	Nonstationary Sparse Spectral Permanental Process. NeurIPS Zicheng Sun, Yixuan Zhang, Zenan Ling, Xuhui Fan, Feng Zhou
2024	J	`jnl`	Nonstationary Sparse Spectral Permanental Process. CoRR Zicheng Sun, Yixuan Zhang, Zenan Ling, Xuhui Fan, Feng Zhou
2023	A	`conf`	Fair Representation Learning with Unreliable Labels. AISTATS Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen
2023	A*	`conf`	Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. NeurIPS Yixuan Zhang, Quyu Kong, Feng Zhou
2023	J	`jnl`	Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. CoRR Yixuan Zhang, Quyu Kong, Feng Zhou
2023	J	`jnl`	Mitigating Label Bias in Machine Learning: Fairness through Confident Learning. CoRR Yixuan Zhang, Boyu Li, Zenan Ling, Feng Zhou
2023	J	`jnl`	Out-of-Distribution Knowledge Distillation via Confidence Amendment. CoRR Zhilin Zhao, Longbing Cao, Yixuan Zhang
2022	J	`jnl`	De-biased Representation Learning for Fairness with Unreliable Labels. CoRR Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen
2022	J	`jnl`	Efficient Inference for Dynamic Flexible Interactions of Neural Populations. J. Mach. Learn. Res. Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu
2021	J	`jnl`	Bias-Tolerant Fair Classification. CoRR Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen
2021	C	`conf`	Bias-tolerant Fair Classification. ACML Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen
2021	A*	`conf`	Efficient Inference of Flexible Interaction in Spiking-neuron Networks. ICLR Feng Zhou, Yixuan Zhang, Jun Zhu
2021	J	`jnl`	Nonlinear Hawkes Processes in Time-Varying System. CoRR Feng Zhou, Quyu Kong, Yixuan Zhang, Cheng Feng, Jun Zhu
2020	J	`jnl`	Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks. CoRR Feng Zhou, Yixuan Zhang, Jun Zhu
2019	—	`conf`	Hawkes Process with Stochastic Triggering Kernel. PAKDD (1) Feng Zhou, Yixuan Zhang, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen