2 citations · 4 across the 13 of their papers we have counts for
10 papers · 1 filter
Understanding Hidden Spatial Preferences in Bayesian Optimization
Maria Bånkestad, Jens Sjölund
Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian-process posterior variance…
BFMT: Enhancing Search Capabilities of Tree Sampler via Bootstrap Flow-Map Tree
Binglin Ji, Anindya Sarkar, Hengchang Lu +4
The ability to efficiently navigate high-dimensional spaces to identify optimal candidates-whether designing targeted molecular structures or generating images with preferred attri…
Sampling Meets Interaction: Sequentially-Controlled Multi-Particle Flow-Maps for Efficient Inference-time Search
Binglin Ji, Anindya Sarkar, Hengchang Lu +2
While generative models have enabled training-free reward alignment, existing particle-based methods are fundamentally constrained by their propensity to local exploration within n…
Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes
Maria Bånkestad, Sanna Jarl, Jens Sjölund
Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary. Despite being a long-recognized artifact in geostatistics and a…
Warm-starting active-set solvers using graph neural networks
Ella J. Schmidtobreick, Daniel Arnström, Paul Häusner +1
Quadratic programming (QP) solvers are widely used in real-time control and optimization, but their computational cost often limits applicability in time-critical settings. To reso…
Learning to accelerate distributed ADMM using graph neural networks
Henri Doerks, Paul Häusner, Daniel Hernández Escobar +1
Distributed optimization is fundamental to large-scale machine learning and control applications. Among existing methods, the alternating direction method of multipliers (ADMM) has…
Exploring Modularity of Agentic Systems for Drug Discovery
Laura van Weesep, Samuel Genheden, Ola Engkvist +1
Large-language models (LLMs) and agentic systems present exciting opportunities to accelerate drug discovery. In this study, we examine the modularity of LLM-based agentic systems…
Efficient Image Restoration with State-Dependent Forward Diffusion
Ziwei Luo, Fredrik K. Gustafsson, Jens Sjölund +1
This paper proposes to perform image restoration through a state-dependent mean-reverting forward diffusion (FoD) process. In contrast to traditional diffusion-based approaches tha…
Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration
Amir Baghi, Jens Sjölund, Joakim Bergdahl +2
Multi-agent reinforcement learning has shown promise in learning cooperative behaviors in team-based environments. However, such methods often demand extensive training time, which…
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair +1
Reducing a graph while preserving its overall properties is an important problem with many applications. Typically, reduction approaches either remove edges (sparsification) or mer…