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Tingwei Meng - Bayesian sampler for inverse problems of a stochastic process by leveraging HJ PDEs
Presenter
- Tingwei Meng
July 17, 2025
IPAM
Guang Lin - Scalable algorithms for Bayesian deep learning via stochastic gradient Monte Carlo
Presenter
- Guang Lin
July 16, 2025
IPAM
Yuanqi Du - Bridging Non-equilibrium Simulation and Probabilistic Machine Learning - IPAM at UCLA
Presenter
- Yuanqi Du
July 16, 2025
IPAM
Jiajia Yu - Learning and Inference in Mean-Field Games - IPAM at UCLA
Presenter
- Jiajia Yu
July 16, 2025
IPAM
Haomin Zhou - A supervised learning scheme for Hamilton-Jacobi equation via density coupling
Presenter
- Haomin Zhou
July 15, 2025
IPAM
Rongjie Lai - Unsupervised In-context Operator Learning for Mean Field Games - IPAM at UCLA
Presenter
- Rongjie Lai
July 15, 2025
IPAM
Siting Liu - Score-Based Generative Models through the Lens of Wasserstein Proximal Operators
Presenter
- Siting Liu
July 15, 2025
IPAM
Li Wang - Learning-enhanced structure preserving particle methods for Landau equation - IPAM at UCLA
Presenter
- Li Wang
July 15, 2025
IPAM
Alex Cloninger - Linearized Optimal Transport to Predict Evolution of Stochastic Particle Systems
Presenter
- Alex Cloninger
July 15, 2025
IPAM
Stan Osher - A Characteristic-Based Deep Learning Framework for Hamilton–Jacobi Equations with Application to Optimal Transport
Presenter
- Stanley Osher
July 14, 2025
IPAM
Wenjing Liao - Exploiting Low-Dimensional Data Structures and Understanding Neural Scaling Laws of Transformers
Presenter
- Wenjing Liao
July 14, 2025
IPAM