6 papers
Continuous-depth Deep Gaussian Processes
Ying Li, Sanyou Wu, Zi Yang +6
Deep Gaussian processes (DGPs) are appealing Bayesian regression models, but their standard discrete-depth parameterization often becomes harder to optimize as depth grows. We argu…
Less Data, Better Timing: Student-Curriculum Coupling for VLM On-Policy Distillation in Temporal Video Grounding
Jiacheng Qiu, Yunsoo Kim, Ruichen Xu +3
On-policy distillation (OPD) provides dense supervision directly on student-generated trajectories, making it an effective post-training strategy for vision-language models in temp…
Simulation-Free Learning of GP-SDEs from Irregular Observations
Zhidi Lin, Yuhao Liu, Ying Li +2
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learn…
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems
Fernando Llorente, Daniel Waxman, Petar M. Djurić
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles o…
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Kurt Butler, Daniel Waxman, Petar M. Djurić
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have…
Scalable Random Feature Latent Variable Models
Ying Li, Zhidi Lin, Yuhao Liu +3
Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent variable models, capable of handling non-Gaussian likelihoods and effectively uncovering pat…