Search Results for author:
Found 14 papers, 6 papers with code
Generalization Error of Graph Neural Networks in the Mean-field Regime
This work provides a theoretical framework for assessing the generalization error of graph classification tasks via graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points.
Mean-field Analysis of Generalization Errors
We propose a novel framework for exploring weak and $L_2$ generalization errors of algorithms through the lens of differential calculus on the space of probability measures.
Nowcasting with signature methods
We look at the nowcasting problem by applying regression on signatures, a simple linear model on these nonlinear objects that we show subsumes the popular Kalman filter.
Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations
In this paper, we prove global convergence for one of the commonly-used deep learning algorithms for solving PDEs, the Deep Galerkin Method (DGM).
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
We here present TAPAS, a toolbox of attacks to evaluate synthetic data privacy under a wide range of scenarios.
Hedging option books using neural-SDE market models
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options.
Synthetic Data -- what, why and how?
This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy.
Neural Q-learning for solving PDEs
We develop a new numerical method for solving elliptic-type PDEs by adapting the Q-learning algorithm in reinforcement learning.
Estimating risks of option books using neural-SDE market models
In this paper, we examine the capacity of an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying.
Identifiability in inverse reinforcement learning
Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions.
Arbitrage-free neural-SDE market models
Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books.
Black-box model risk in finance
Machine learning models are increasingly used in a wide variety of financial settings.
Asymptotic Randomised Control with applications to bandits
We consider a general multi-armed bandit problem with correlated (and simple contextual and restless) elements, as a relaxed control problem.
Detecting and repairing arbitrage in traded option prices
In addition, we show that removing arbitrage from prices data by our repair method can improve model calibration with enhanced robustness and reduced calibration error.
