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What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Azar y Aritmetica
Harald Andres Helfgott · 2009-09-05 · via math.ST updates on arXiv.org

Let omega(n) be the number of prime divisors of an integer n. Let n be an integer taken at random between 1 and N. What can be said about the value then taken by omega(n)? What is its expected value? What is its distribution in the limit? What is the probability that omega(n) will deviate greatly from its expected value? We will study these questions as an introduction to probabilistic number theory. We treat several central topics in probabilistic number theory without assuming previous knowledge of the area. Neither measure theory nor complex analysis are assumed. In the exercises, among other topics, we develop some of the bases of sieve theory as an application of probabilistic ideas. ----- Sea omega(n) el numero de divisores primos de un entero n. Sea n un entero tomado al azar entre 1 y N. Que se puede decir del valor que entonces tomara' omega(n)? Cual es su esperanza? Cual es su distribucion en el limite? Cual es la probabilidad que omega(n) tome valores que se alejen mucho de su esperanza? Estudiamos estas preguntas a guisa de introduccion a la teoria de numeros probabilistica. Trataremos varios topicos centrales de la teoria de probabilidades sin suponer conocimientos previos en el area. No asumiremos ni teoria de la medida ni analisis complejo. En los ejercicios, entre otros topicos, se desarrollaran las bases de la teoria de cribas como una aplicacion de ideas probabilisticas.