47 citations · 219 across the 37 of their papers we have counts for
12 papers · 1 filter
Gaussian Flow Dynamics: Simulation-Free Neural SDE Learning Beyond One-Time Marginals
Grigory Bartosh, Christian A. Naesseth
Simulation-free training of latent Stochastic Differential Equations (SDEs) relies on a variational posterior process whose one-time marginals are tractable, typically Gaussian. Su…
SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Maria Marchenko, Martin Andrae, Fredrik Lindsten +1
Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-ra…
On the Tightness and Computational Tractability of Higher-Dimensional Confidence Sequences
Fabian Denoodt, Sibylle Hess, Joaquin Vanschoren +1
Modern sequential monitoring problems often involve multiple metrics, where we monitor several data streams simultaneously and may act once the evidence is strong enough. Confidenc…
Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models
Stipe Frković, Metod Jazbec, Christian A. Naesseth
Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidence-based samplers. However, r…
Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo
Lohithsai Yadala Chanchu, Hany Abdulsamad, Christian A. Naesseth
We study inference-time control for text generation in discrete diffusion language models, where the goal is to steer sampling toward sequence-level rewards without retraining. Pri…
Online Safety Monitoring for LLMs
Mona Schirmer, Metod Jazbec, Alexander Timans +3
Despite alignment training, LLMs remain prone to generating unsafe outputs at deployment time. Monitoring outputs online and raising an alarm when safety can no longer be assumed i…