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From the 1 of 28 linked papers with an AI index.

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28 papers

q-fin.MF2026

DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces

Hans Buehler, Blanka Horvath, Anastasis Kratsios

This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage. Our model is…

math.PR2026

NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes

Anastasis Kratsios, Giulia Livieri, Philipp Schmocker

The paper proposes NeuralChaos, a neural operator architecture that efficiently approximates predictable square‑integrable stochastic processes using finitely many Brownian motion…

cs.LG2026

Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

Anastasis Kratsios, Simone Brugiapaglia, Bum Jun Kim +2

Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily capture…

q-fin.CP2026

PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable Jäckel Operator

Raeid Saqur, Yannick Limmer, Anastasis Kratsios +2

Modern option-learning systems operate in two coordinates: price space, where markets quote and no-arbitrage constraints are most naturally enforced, and implied volatility (IV) sp…

cs.LG2026

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization

Mikhail Persiianov, Arip Asadulaev, Nikita Andreev +5

Learning conditional distributions is a central problem in machine learning, which is typically approached via supervised methods with paired data

stat.ML2026

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits

Anastasis Kratsios, Giulia Livieri, A. Martina Neuman

We study the statistical behavior of reasoning probes in a stylized model of iterative computation inspired by neural algorithmic reasoning. The underlying computation is given by…