74 citations · 120 across the 41 of their papers we have counts for
7 papers · 1 filter
Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
Takashi Furuya, Anastasis Kratsios
Neural operator (NO) architectures learn nonlinear maps between infinite-dimensional function spaces and are widely used to accelerate simulation and enable data-driven model disco…
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 .…
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
Ruiyang Hong, Anastasis Kratsios
The foundations of deep learning are supported by the seemingly opposing perspectives of approximation or learning theory. The former advocates for large/expressive models that nee…
Neural Spacetimes for DAG Representation Learning
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law +2
We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as…
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach
Reza Arabpour, John Armstrong, Luca Galimberti +2
Predicting the conditional evolution of Volterra processes with stochastic volatility is a crucial challenge in mathematical finance. While deep neural network models offer promise…
Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces
Anastasis Kratsios, A. Martina Neuman, Gudmund Pammer
Machine learning models with inputs in a Euclidean space , when implemented on digital computers, generalize, and their generalization gap converges to at a rate…
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya +1
Mixture-of-Experts (MoEs) can scale up beyond traditional deep learning models by employing a routing strategy in which each input is processed by a single "expert" deep learning m…