Deep Generative Model in Machine Learning: Theory, Principle and Efficacy （2nd Workshop）

The 2nd Deep Generative Models in Machine Learning: Theories, Principles, and Efficacy (DeLTa 2026) workshop aims to bridge the gap between theory and practice in modern generative modeling. Deep Generative Models (DGMs)—including VAEs, GANs, flows, autoregressive, and diffusion models—have transformed AI research, yet fundamental theoretical and algorithmic challenges persist. DeLTa 2026 will bring together experts across statistics, optimization, and deep learning to address two central questions: (1) How can we develop unified theoretical frameworks to understand and design advanced generative models? and (2) How can we improve their efficiency, reliability, and transferability in real-world applications? This year’s workshop expands its scope to include emerging frontiers such as flow matching, stochastic control, discrete and low-dimensional diffusion models, post-training theory, and large language diffusion models. By fostering dialogue between theoretical and applied communities, DeLTa 2026 seeks to establish principled foundations that guide scalable, interpretable, and safe generative modeling. The workshop will feature invited talks, contributed papers, and a dedicated short-paper track to encourage participation from early-career and underrepresented researchers. Building on the success of DeLTa 2025, we anticipate over 400 participants and vibrant interdisciplinary engagement at ICLR 2026.