Dmitry Vetrov
Dmitry Vetrov (graduated from Moscow State Univerisity in 2003, PhD in 2006) is a professor of Computer Science at Constructor University, Bremen. He is a founder and the head of the Bayesian Methods Research Group. Three of his recent PhD students became researchers at DeepMind. His research focuses on combining Bayesian framework with Deep Learning models. His group is also actively involved in developing more efficient algorithms for diffusion models, studying the properties of loss landscape in deep neural networks, building scalable tools for stochastic optimization, application of tensor decomposition methods to large-scale Machine Learning, improving conditional text generation models, etc.
Recent Publications
- Why Gaussian Diffusion Models Fail on Discrete Data? (2026) CoLM 2026
- Can Training Dynamics of Scale-Invariant Neural Networks Be Explained by the Thermodynamics of an Ideal Gas? (2026) ICJAI 2026
- One-step Optimal Transport via Regularized Distribution Matching Distillation (2026) ICML 2026
- Guided Star-Shaped Masked Diffusion (2026) ICML 2026
- Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation (2026) ICML 2026
- GeomMotif: A Benchmark for Arbitrary Geometric Preservation in Protein Generation (2026) ICLR 2026
- Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion (2026) AAAI 2026
- How to Train Your Latent Diffusion Language Model Jointly With the Latent Space (2026) ICML 2026 Workshop
- Diffusion on Language Model Encodings for Protein Sequence Generation (2025) ICML 2025
- SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations (2025) ICML 2025
- Cosmos: Compressed and Smooth Latent Space for Text Diffusion Modeling (2025) NeurIPS 2025
- TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings (2025) AAAI 2025
- Adaptive Destruction Processes for Diffusion Samplers (2025) NeurIPS 2025 Workshop
- Where Do Large Learning Rates Lead Us? (2024) NeurIPS 2024
- Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling (2024) NeurIPS 2024

