11 citations · 16 across the 9 of their papers we have counts for
7 papers · 1 filter
Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization
Jatan Shrestha, Santeri Heiskanen, Kari Hepola +3
Multi-objective optimization (MOO) arises in many real-world applications where trade-offs between competing objectives must be carefully balanced. In the offline setting, where on…
Softly Constrained Denoisers for Diffusion Models Applied to Partial Differential Equations
Victor M. Yeom-Song, Severi Rissanen, Arno Solin +2
Diffusion models have become a powerful generative prior for solutions of partial differential equations (PDEs). Existing approaches enforce physical constraints either by adding t…
Progressive Tempering Sampler with Diffusion
Severi Rissanen, RuiKang OuYang, Jiajun He +4
Recent research has focused on designing neural samplers that amortize the process of sampling from unnormalized densities. However, despite significant advancements, they still fa…
Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs
Severi Rissanen, Markus Heinonen, Arno Solin
The covariance for clean data given a noisy observation is an important quantity in many training-free guided generation methods for diffusion models. Current methods require heavy…
Improving Discrete Diffusion Models via Structured Preferential Generation
Severi Rissanen, Markus Heinonen, Arno Solin
In the domains of image and audio, diffusion models have shown impressive performance. However, their application to discrete data types, such as language, has often been suboptima…
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
Najwa Laabid, Severi Rissanen, Markus Heinonen +2
Graph diffusion models, dominant in graph generative modeling, remain underexplored for graph-to-graph translation tasks like chemical reaction prediction. We demonstrate that stan…
A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models
Severi Rissanen, Pekka Marttinen
Using deep latent variable models in causal inference has attracted considerable interest recently, but an essential open question is their ability to yield consistent causal estim…