arXiv Machine Learning
LATS: Levy Adaptive Tree Sampling for Feedback-Driven Diverse Target Discovery
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Jul 7
Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search
arXiv:2607. 02915v1 Announce Type: cross Abstract: In many scientific and engineering domains, maximizing discovery within a limited sampling budget demands strategic, observation-guided exploration.
arXiv AI
Sep 17
Accelerating Diffusion Sampling via Speculative Draft Trees
The paper introduces "draft trees" to accelerate diffusion model sampling by allowing non‑linear lookahead drafts, thereby increasing acceptance rates per expensive target evaluation. It connects speculative sampling to relative entropy coding, adopts greedy rejection sampling as the draft‑target coupling, and demonstrates up to 8.3% speed‑up over reflection coupling baselines in experiments.
arXiv Machine Learning
Jun 26
Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space
arXiv:2606. 26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example.
arXiv Machine Learning
Aug 31
Improved off-policy training of diffusion samplers
The paper investigates training diffusion models to sample from distributions defined by unnormalized densities or energy functions. It benchmarks various diffusion-structured inference techniques, including simulation-based variational methods and off-policy approaches such as continuous generative flow networks, highlighting their relative strengths and challenging some prior claims. Additionally, the authors introduce a new exploration strategy for off-policy methods that employs local search in the target space with a replay buffer, demonstrating improved sample quality across multiple target distributions.
arXiv AI
Jun 10
Towards Diverse Scientific Hypothesis Search with Large Language Models
arXiv:2606. 10587v1 Announce Type: cross Abstract: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses.
arXiv Machine Learning
Jun 2
Do Heavy Tails Help Diffusion? On the Subtle Trade-off Between Initialization and Training
arXiv:2605. 13175v2 Announce Type: replace Abstract: Recent works have proposed incorporating heavy-tailed (HT) noise into diffusion- and flow-based generative models, with the goals of better recovering the tails of target distributions and improving generative diversity.