108 citations · 131 across the 9 of their papers we have counts for
10 papers
Understanding Transfer Learning via Mean-field Analysis
Gholamali Aminian, Łukasz Szpruch, Samuel N. Cohen
We propose a novel framework for exploring generalization errors of transfer learning through the lens of differential calculus on the space of probability measures. In particular,…
Generalization Error of Graph Neural Networks in the Mean-field Regime
Gholamali Aminian, Yixuan He, Gesine Reinert +2
This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasse…
Mean-field Analysis of Generalization Errors
Gholamali Aminian, Samuel N. Cohen, Łukasz Szpruch
We propose a novel framework for exploring weak and generalization errors of algorithms through the lens of differential calculus on the space of probability measures. Specif…
A Framework for Auditable Synthetic Data Generation
Florimond Houssiau, Samuel N. Cohen, Lukasz Szpruch +5
Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that enable the synthesis of high-dimensional datasets. However, many generation techn…
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
Florimond Houssiau, James Jordon, Samuel N. Cohen +6
Personal data collected at scale promises to improve decision-making and accelerate innovation. However, sharing and using such data raises serious privacy concerns. A promising so…
Synthetic Data -- what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau +5
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. The artic…
Identifiability in inverse reinforcement learning
Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch
Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed in Russell [1998]…
Black-box model risk in finance
Samuel N. Cohen, Derek Snow, Lukasz Szpruch
Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide…
On Markovian solutions to Markov Chain BSDEs
Samuel N. Cohen, Lukasz Szpruch
We study (backward) stochastic differential equations with noise coming from a finite state Markov chain. We show that, for the solutions of these equations to be `Markovian', in t…
A limit order book model for latency arbitrage
Samuel N. Cohen, Lukasz Szpruch
We consider a single security market based on a limit order book and two investors, with different speeds of trade execution. If the fast investor can front-run the slower investor…