arXiv:2509.16337v2 Announce Type: replace-cross Abstract: Multi-centre studies increasingly rely on distributed inference, where sites share only centre-level summaries. Homogeneity of parameters across centres is often violated, motivating methods that both emph{test} for equality and emph{learn} centre groupings before estimation. We develop multivariate Cochran-type tests that operate on summary statistics and embed them in a sequential, test-driven emph{Clusters-of-Centres (CoC)} algorithm that merges centres (or blocks) only when equality is not rejected. We derive the asymptotic $chi^2$-mixture distributions of the […]

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arXiv:2501.15910v3 Announce Type: replace-cross Abstract: We study the sample complexity of online reinforcement learning in the general hzyrev{non-episodic} setting of nonlinear dynamical systems with continuous state and action spaces. Our analysis accommodates a large class of dynamical systems ranging from a finite set of nonlinear candidate models to models with bounded and Lipschitz continuous dynamics, to systems that are parametrized by a compact and real-valued set of parameters. In the most general setting, our algorithm achieves a policy […]

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arXiv:2303.12957v2 Announce Type: replace-cross Abstract: Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal. This paper formalizes exogenous state variables and rewards and shows that if the reward function decomposes additively into endogenous and exogenous components, the MDP can be decomposed into an exogenous Markov Reward Process (based on the exogenous reward) and an endogenous Markov Decision Process (optimizing the endogenous reward). Any optimal policy for the endogenous MDP is […]

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arXiv:2604.00683v1 Announce Type: cross Abstract: Stochastic natural gradient variational inference (NGVI) is a popular and efficient algorithm for Bayesian inference. Despite empirical success, the convergence of this method is still not fully understood. In this work, we define and study a projected stochastic NGVI when variational distributions form an exponential family. Stochasticity arises when either gradients are intractable expectations or large sums. We prove new non-asymptotic convergence results for combinations of constant or decreasing step sizes and constant […]

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arXiv:2603.26748v1 Announce Type: new Abstract: This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain […]

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The secret to reliable AI agents isn’t better prompts. It’s wrapping a probabilistic core inside a deterministic shell — and knowing exactly where the boundary sits. The Pattern Nobody Named Every architecture decision I’ve made over the past year — skill servers, model routing, meta-tools, context engineering, the 9-tool framework — follows the same underlying principle. I never named it explicitly, but it’s been the thread connecting everything. Here it is: the LLM is the core, not the system. The system is deterministic. The core is […]

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Comparison of current rating methods popular on the web alongside a technical breakdown of a new novel rating algorithm used in the Beli App. Its supposed to be a book shelf with a new insertion being determined by a binary search I got an ad on Instagram for an app called Beli. Upon first glance, it’s a social media app for restaurant patrons aimed at a similar niche as Untapped but with an emphasis on the restaurant instead […]

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