7 papers
Decafs: Disentangled Conditional adversarial Flows
Anirudh jain, Sakshi Varshney, Samuel Kaski +1
Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particul…
Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
Abhi Gupta, Polina Barabanshchikova, Vikas Garg +2
The abundance of pre-trained diffusion models provides an opportunity for composition. Combining several models, however, runs the risk of one model dominating or models disagreein…
Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski
Preferential Bayesian optimization (PBO) learns latent utilities from pairwise comparisons, but most existing methods assume homoscedastic comparison noise. This is inadequate in h…
A Theory of Random Graph Shift in Truncated-Spectrum vRKHS
Zhang Wan, Tingting Mu, Samuel Kaski
This paper develops a theory of graph classification under domain shift through a random-graph generative lens, where we consider intra-class graphs sharing the same random graph m…
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?
RafaÅ Karczewski, Samuel Kaski, Markus Heinonen +1
Several generative models with elaborate training and sampling procedures have been proposed to accelerate structure-based drug design (SBDD); however, their empirical performance…