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Be Simple | corrode Rust Consulting
The phone buzzes at 3 AM. You roll out of bed, open your laptop, and see this in the logs: th…

Reinforced sequential Monte Carlo for amortised sampling
Sanghyeok Choi, Sarthak Mittal, V\'ictor Elvira, Jinkyoo Park, Nikolay Malkin
https://arxiv.org/abs/2510.11711 https…

Reinforced sequential Monte Carlo for amortised sampling
This paper proposes a synergy of amortised and particle-based methods for sampling from distributions defined by unnormalised density functions. We state a connection between sequential Monte Carlo (SMC) and neural sequential samplers trained by maximum-entropy reinforcement learning (MaxEnt RL), wherein learnt sampling policies and value functions define proposal kernels and twist functions. Exploiting this connection, we introduce an off-policy RL training procedure for the sampler that uses …

Wijers moest wel sneuvelen want hij was een obstakel in het plan van ondernemers voor een rechts kabinet - Joop - BNNVARA
#NLpol #Wijers

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
https://arxiv.org/abs/2510.12088

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where the agent has only "one life" to explore a hostile environment without human guidance. We introduce OneLife, a framework that models world…

A Black-Box Debiasing Framework for Conditional Sampling
Han Cui, Jingbo Liu
https://arxiv.org/abs/2510.11071 https://arxiv.org/pdf/2510.11071

A Black-Box Debiasing Framework for Conditional Sampling
Conditional sampling is a fundamental task in Bayesian statistics and generative modeling. Consider the problem of sampling from the posterior distribution $P_{X|Y=y^*}$ for some observation $y^*$, where the likelihood $P_{Y|X}$ is known, and we are given $n$ i.i.d. samples $D=\{X_i\}_{i=1}^n$ drawn from an unknown prior distribution $π_X$. Suppose that $f(\hatπ_{X^n})$ is the distribution of a posterior sample generated by an algorithm (e.g. a conditional generative model or the Ba…

Så vått, so what?

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Kaiwen He, Petros Drineas, Rajiv Khanna
https://arxiv.org/abs/2510.12669 https://arxiv.or…

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampling edges proportionally to their effective resistances, but require expensive preprocessing to estimate these resistances. We study whether uniform edge sampling-a simple, structure-agnostic strategy-can suffice for spectral clustering. Our main result shows that for graphs adm…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit, Vadim Indelman
https://arxiv.org/abs/2509.10162 https://…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are widely adopted for their ability to approximate optimal actions using a generative model. However, in practical settings, the generative model is often learned from limited data, introducing approxi…

KAN-SR: A Kolmogorov-Arnold Network Guided Symbolic Regression Framework
Marco Andrea B\"uhler, Gonzalo Guill\'en-Gos\'albez
https://arxiv.org/abs/2509.10089 https:…

KAN-SR: A Kolmogorov-Arnold Network Guided Symbolic Regression Framework
We introduce a novel symbolic regression framework, namely KAN-SR, built on Kolmogorov Arnold Networks (KANs) which follows a divide-and-conquer approach. Symbolic regression searches for mathematical equations that best fit a given dataset and is commonly solved with genetic programming approaches. We show that by using deep learning techniques, more specific KANs, and combining them with simplification strategies such as translational symmetries and separabilities, we are able to recover grou…