| 2024 | Multi-resolution Time-Series Transformer for Long-term Forecasting. | Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates |
| 2024 | Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees. | Zhi Zhang, Weijian Li, Han Liu |
| 2024 | Online Learning in Contextual Second-Price Pay-Per-Click Auctions. | Mengxiao Zhang, Haipeng Luo |
| 2024 | Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. | Siqi Zhang, Yifan Hu, Liang Zhang, Niao He |
| 2024 | Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods. | Jiaxin Zhang, Kamalika Das, Kumar Sricharan |
| 2024 | Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures. | Mingyuan Zhang, Shivani Agarwal |
| 2024 | Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization. | Zhou Zhai, Wanli Shi, Heng Huang, Yi Chang, Bin Gu |
| 2024 | Deep Learning-Based Alternative Route Computation. | Alex Zhai, Dee Guo, Sreenivas Gollapudi, Kostas Kollias, Daniel Delling |
| 2024 | SDMTR: A Brain-inspired Transformer for Relation Inference. | Xiangyu Zeng, Jie Lin, Piao Hu, Zhihao Li, Tianxi Huang |
| 2024 | Communication-Efficient Federated Learning With Data and Client Heterogeneity. | Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze, Dan Alistarh |
| 2024 | Learning multivariate temporal point processes via the time-change theorem. | Guilherme Augusto Zagatti, See-Kiong Ng, Stphane Bressan |
| 2024 | Riemannian Laplace Approximation with the Fisher Metric. | Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami |
| 2024 | Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers. | Yuya Yoshikawa, Tomoharu Iwata |
| 2024 | Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models. | Soheun Yi, Sanghack Lee |
| 2024 | Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. | Zhenzhang Ye, Gabriel Peyr, Daniel Cremers, Pierre Ablin |
| 2024 | Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning. | Zeqi Ye, Hansheng Jiang |
| 2024 | Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow. | Rentian Yao, Linjun Huang, Yun Yang |
| 2024 | Simulation-Based Stacking. | Yuling Yao, Bruno Rgaldo-Saint Blancard, Justin Domke |
| 2024 | Causal Bandits with General Causal Models and Interventions. | Zirui Yan, Dennis Wei, Dmitriy A. Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer |
| 2024 | Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data. | Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash |
| 2024 | Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes. | Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh |
| 2024 | Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles. | Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley |
| 2024 | Hodge-Compositional Edge Gaussian Processes. | Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi |
| 2024 | Learning Fair Division from Bandit Feedback. | Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki |
| 2024 | Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting. | Siyuan Xu, Yucheng Wang, Mingzhou Fan, Byung-Jun Yoon, Xiaoning Qian |