digitado

arXiv:2604.14975v1 Announce Type: cross Abstract: To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implementation details for the iterative process, including the regularized optimization algorithm and the geometric search cross-validation tuning algorithm. Three distinct penalty methods, Lasso, Ridge, and Elastic-net […]

Ver mais

arXiv:2604.14908v1 Announce Type: cross Abstract: We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold $tau_r$ and cast joint beam and rate adaptation as a combinatorial semi-bandit […]

Ver mais

arXiv:2604.14587v1 Announce Type: cross Abstract: Lion optimizer is a popular learning-based optimization algorithm in machine learning, which shows impressive performance in training many deep learning models. Although convergence property of the Lion optimizer has been studied, its generalization analysis is still missing. To fill this gap, we study generalization property of the Lion via algorithmic stability based on the mathematical induction. Specifically, we prove that the Lion has a generalization error of $O(frac{1}{Ntau^T})$, where $N$ is training sample […]

Ver mais

arXiv:2604.14809v1 Announce Type: new Abstract: We study a classification problem with three key challenges: pervasive informative missingness, the integration of partial prior expert knowledge into the learning process, and the need for interpretable decision rules. We propose a framework that encodes prior knowledge through an expert-guided class-conditional model for one or more classes, and use this model to construct a small set of interpretable goodness-of-fit features. The features quantify how well the observed data agree with the expert […]

Ver mais

arXiv:2604.14322v1 Announce Type: new Abstract: We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is misspecified. Leveraging recent advances in generalised Bayesian inference, we define robustness via the posterior influence function (PIF), and provide conditions under which the online iHMM has bounded PIF. Imposing robustness inevitably induces an adaptation lag for regime switching. Our method, which is called Batched Robust iHMM (BR-iHMM), balances […]

Ver mais

arXiv:2604.13183v2 Announce Type: new Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its core difficulty lies in severe semantic inconsistency caused by viewpoint variation and poor generalization under domain shift. Existing methods mainly rely on 2D correspondence, but they are easily distracted by redundant shared information across views, leading to less transferable representations. To address this, we propose GeoLink, a 3D-aware semantic-consistent framework for Generalizable cross-view […]

Ver mais

Hey r/reinforcementlearning, I built a highly parallel CUDA implementation of Policy Iteration for continuous state/action spaces using barycentric interpolation. It solves complex systems like an Overhead Crane and Double CartPole without relying on standard deep RL methods. I’ve been working on this based on the theoretical framework from “Continuous RL via Dynamic Programming” (Dupuis & Kushner). I’m sharing it here because I think this approach is heavily underrepresented compared to DQN/PPO and deserves more attention. Most RL implementations […]

Ver mais

In this tutorial, we build an advanced, production-ready agentic system using SmolAgents and demonstrate how modern, lightweight AI agents can reason, execute code, dynamically manage tools, and collaborate across multiple agents. We start by installing dependencies and configuring a powerful yet efficient LLM backend, and then progressively design custom tools, including mathematical utilities, memory storage, and web search capabilities. We explore both CodeAgent and ToolCallingAgent paradigms, understand how tools are managed dynamically through the agent.tools dictionary, and implement […]

Ver mais

In this tutorial, we build a universal long-term memory layer for AI agents using Mem0, OpenAI models, and ChromaDB. We design a system that can extract structured memories from natural conversations, store them semantically, retrieve them intelligently, and integrate them directly into personalized agent responses. We move beyond simple chat history and implement persistent, user-scoped memory with full CRUD control, semantic search, multi-user isolation, and custom configuration. Finally, we construct a production-ready memory-augmented agent architecture that demonstrates how […]

Ver mais