Point processes with event time uncertainty
Fuente:
arXiv
Saved in:
| Main Authors: | Cheng, Xiuyuan, Gong, Tingnan, Xie, Yao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Deep spatio-temporal point processes: Advances and new directions
by: Cheng, Xiuyuan, et al.
Published: (2025)
by: Cheng, Xiuyuan, et al.
Published: (2025)
Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025)
by: Xie, Yao, et al.
Published: (2025)
Kernel Two-Sample Tests for Manifold Data
by: Cheng, Xiuyuan, et al.
Published: (2021)
by: Cheng, Xiuyuan, et al.
Published: (2021)
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021)
by: Cheng, Xiuyuan, et al.
Published: (2021)
Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026)
by: Cheng, Xiuyuan, et al.
Published: (2026)
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
by: Cheng, Xiuyuan, et al.
Published: (2022)
by: Cheng, Xiuyuan, et al.
Published: (2022)
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)
by: Cheng, Xiuyuan, et al.
Published: (2023)
Improved convergence rate of kNN graph Laplacians: differentiable self-tuned affinity
by: Cheng, Xiuyuan, et al.
Published: (2024)
by: Cheng, Xiuyuan, et al.
Published: (2024)
Neural network-based CUSUM for online change-point detection
by: Gong, Tingnan, et al.
Published: (2022)
by: Gong, Tingnan, et al.
Published: (2022)
Higher-criticism for sparse multi-stream change-point detection
by: Gong, Tingnan, et al.
Published: (2024)
by: Gong, Tingnan, et al.
Published: (2024)
Online Kernel CUSUM for Change-Point Detection
by: Wei, Song, et al.
Published: (2022)
by: Wei, Song, et al.
Published: (2022)
Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
by: Taheri, Mahsa, et al.
Published: (2022)
by: Taheri, Mahsa, et al.
Published: (2022)
On uncertainty-penalized Bayesian information criterion
by: Thanasutives, Pongpisit, et al.
Published: (2024)
by: Thanasutives, Pongpisit, et al.
Published: (2024)
Incorporating structural uncertainty in causal decision making
by: Kaptein, Maurits
Published: (2025)
by: Kaptein, Maurits
Published: (2025)
A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)
by: Tyralis, Hristos, et al.
Published: (2022)
Variational bagging: a robust approach for Bayesian uncertainty quantification
by: Fan, Shitao, et al.
Published: (2025)
by: Fan, Shitao, et al.
Published: (2025)
Kernel-based Optimally Weighted Conformal Time-Series Prediction
by: Lee, Jonghyeok, et al.
Published: (2024)
by: Lee, Jonghyeok, et al.
Published: (2024)
Score-based generative models are provably robust: an uncertainty quantification perspective
by: Mimikos-Stamatopoulos, Nikiforos, et al.
Published: (2024)
by: Mimikos-Stamatopoulos, Nikiforos, et al.
Published: (2024)
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
by: Cheng, Ziheng, et al.
Published: (2025)
by: Cheng, Ziheng, et al.
Published: (2025)
Error Analysis of Discrete Flow with Generator Matching
by: Wan, Zhengyan, et al.
Published: (2025)
by: Wan, Zhengyan, et al.
Published: (2025)
Causal inference for the expected number of recurrent events in the presence of a terminal event
by: Baer, Benjamin R., et al.
Published: (2023)
by: Baer, Benjamin R., et al.
Published: (2023)
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
by: Bieringer, Sebastian, et al.
Published: (2023)
by: Bieringer, Sebastian, et al.
Published: (2023)
PCA for Point Processes
by: Picard, Franck, et al.
Published: (2024)
by: Picard, Franck, et al.
Published: (2024)
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
by: Graf, Maximilian, et al.
Published: (2026)
by: Graf, Maximilian, et al.
Published: (2026)
Adaptive sparse variational approximations for Gaussian process regression
by: Nieman, Dennis, et al.
Published: (2025)
by: Nieman, Dennis, et al.
Published: (2025)
Bias-Corrected Joint Spectral Embedding for Multilayer Networks with Invariant Subspace: Entrywise Eigenvector Perturbation and Inference
by: Xie, Fangzheng
Published: (2024)
by: Xie, Fangzheng
Published: (2024)
Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
by: Arpino, Gabriel, et al.
Published: (2024)
by: Arpino, Gabriel, et al.
Published: (2024)
Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points
by: Ezoe, Haruka, et al.
Published: (2026)
by: Ezoe, Haruka, et al.
Published: (2026)
Supervised Kernel Thinning
by: Gong, Albert, et al.
Published: (2024)
by: Gong, Albert, et al.
Published: (2024)
Robust Alignment via Partial Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2025)
by: Gong, Xiaoyun, et al.
Published: (2025)
Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
by: Jiang, Hanyang, et al.
Published: (2026)
by: Jiang, Hanyang, et al.
Published: (2026)
Generalising realisability in statistical learning theory under epistemic uncertainty
by: Cuzzolin, Fabio
Published: (2024)
by: Cuzzolin, Fabio
Published: (2024)
On two ways to use determinantal point processes for Monte Carlo integration
by: Gautier, Guillaume, et al.
Published: (2026)
by: Gautier, Guillaume, et al.
Published: (2026)
Scalable multitask Gaussian processes for complex mechanical systems with functional covariates
by: Gninkou, Razak Christophe Sabi, et al.
Published: (2026)
by: Gninkou, Razak Christophe Sabi, et al.
Published: (2026)
Transfer Learning for Causal Effect Estimation
by: Wei, Song, et al.
Published: (2023)
by: Wei, Song, et al.
Published: (2023)
Robust Point Matching with Distance Profiles
by: Hur, YoonHaeng, et al.
Published: (2023)
by: Hur, YoonHaeng, et al.
Published: (2023)
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by: Stankewitz, Bernhard, et al.
Published: (2024)
by: Stankewitz, Bernhard, et al.
Published: (2024)
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
by: Liu, Xingchi, et al.
Published: (2024)
by: Liu, Xingchi, et al.
Published: (2024)
Duality induced by an embedding structure of determinantal point process
by: Hino, Hideitsu, et al.
Published: (2024)
by: Hino, Hideitsu, et al.
Published: (2024)
AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
by: Zhang, Huiqi, et al.
Published: (2025)
by: Zhang, Huiqi, et al.
Published: (2025)
Similar Items
-
Deep spatio-temporal point processes: Advances and new directions
by: Cheng, Xiuyuan, et al.
Published: (2025) -
Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025) -
Kernel Two-Sample Tests for Manifold Data
by: Cheng, Xiuyuan, et al.
Published: (2021) -
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021) -
Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026)