Zobrazeno 1 - 10
of 2 333
pro vyhledávání: '"XIE, Yao"'
Autor:
Jiang, Hanyang, Xie, Yao
Reliable uncertainty quantification at unobserved spatial locations, especially in the presence of complex and heterogeneous datasets, remains a core challenge in spatial statistics. Traditional approaches like Kriging rely heavily on assumptions suc
Externí odkaz:
http://arxiv.org/abs/2412.01098
In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages. Building resilience, the ability to withstand, adapt to, and recover from major disruptions, has become crucial for the
Externí odkaz:
http://arxiv.org/abs/2411.17099
Point processes are widely used statistical models for uncovering the temporal patterns in dependent event data. In many applications, the event time cannot be observed exactly, calling for the incorporation of time uncertainty into the modeling of p
Externí odkaz:
http://arxiv.org/abs/2411.02694
Flow Matching (FM) is a simulation-free method for learning a continuous and invertible flow to interpolate between two distributions, and in particular to generate data from noise in generative modeling. In this paper, we introduce Local Flow Matchi
Externí odkaz:
http://arxiv.org/abs/2410.02548
Posterior sampling in high-dimensional spaces using generative models holds significant promise for various applications, including but not limited to inverse problems and guided generation tasks. Despite many recent developments, generating diverse
Externí odkaz:
http://arxiv.org/abs/2410.02078
Autor:
Wu, Dongze, Xie, Yao
Sampling from high dimensional, multimodal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics based machine learning. In this paper, we propose Annealing Flow, a continuous normalizing flow
Externí odkaz:
http://arxiv.org/abs/2409.20547
We present a procedure based on higher criticism (Dohono \& Jin 2004) to address the sparse multi-sensor quickest change-point detection problem. Namely, we aim to detect a change in the distribution of the multi-sensor that might affect a few sensor
Externí odkaz:
http://arxiv.org/abs/2409.15597
Self-exciting point processes are widely used to model the contagious effects of crime events living within continuous geographic space, using their occurrence time and locations. However, in urban environments, most events are naturally constrained
Externí odkaz:
http://arxiv.org/abs/2409.10882
Police patrol units need to split their time between performing preventive patrol and being dispatched to serve emergency incidents. In the existing literature, patrol and dispatch decisions are often studied separately. We consider joint optimizatio
Externí odkaz:
http://arxiv.org/abs/2409.02246
Despite the growing prevalence of artificial neural networks in real-world applications, their vulnerability to adversarial attacks remains a significant concern, which motivates us to investigate the robustness of machine learning models. While vari
Externí odkaz:
http://arxiv.org/abs/2408.09672