The NEU Meta-Algorithm for Geometric Learning with Applications in Finance. - Anastasis Kratsios; Cody B. Hyndman (CoRR, 2018)

Universal Approximation Theorems. - Anastasis Kratsios (CoRR, 2019)

Non-Euclidean Universal Approximation. - Anastasis Kratsios; Ievgen Bilokopytov (NeurIPS, 2020)

Architopes: An Architecture Modification for Composite Pattern Learning, Increased Expressiveness, and Reduced Training Time. - Anastasis Kratsios; Behnoosh Zamanlooy (CoRR, 2020)

Overcoming The Limitations of Neural Networks in Composite-Pattern Learning with Architopes. - Anastasis Kratsios; Behnoosh Zamanlooy (CoRR, 2020)

Optimizing Optimizers: Regret-optimal gradient descent algorithms. - Philippe Casgrain; Anastasis Kratsios (COLT, 2021)

Generative OrnsteinUhlenbeck Markets via Geometric Deep Learning. - Anastasis Kratsios; Cody B. Hyndman (GSI (2), 2021)

The Universal Approximation Property. - Anastasis Kratsios (Ann. Math. Artif. Intell., 2021)

Quantitative Rates and Fundamental Obstructions to Non-Euclidean Universal Approximation with Deep Narrow Feed-Forward Networks. - Anastasis Kratsios; Leonie Papon (CoRR, 2021)

Universal Regular Conditional Distributions via Probability Measure-Valued Deep Neural Models. - Anastasis Kratsios (CoRR, 2021)

NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation. - Anastasis Kratsios; Cody B. Hyndman (J. Mach. Learn. Res., 2021)

Universal Approximation Under Constraints is Possible with Transformers. - Anastasis Kratsios; Behnoosh Zamanlooy; Tianlin Liu; Ivan Dokmanic (ICLR, 2022)

Metric Hypertransformers are Universal Adapted Maps. - Beatrice Acciaio; Anastasis Kratsios; Gudmund Pammer (CoRR, 2022)

Piecewise-Linear Activations or Analytic Activation Functions: Which Produce More Expressive Neural Networks? - Anastasis Kratsios; Behnoosh Zamanlooy (CoRR, 2022)

Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis. - Luca Galimberti; Giulia Livieri; Anastasis Kratsios (CoRR, 2022)

Learning sub-patterns in piecewise continuous functions. - Anastasis Kratsios; Behnoosh Zamanlooy (Neurocomputing, 2022)

Universal Approximation Theorems for Differentiable Geometric Deep Learning. - Anastasis Kratsios; Leonie Papon (J. Mach. Learn. Res., 2022)

Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions? - Anastasis Kratsios; Behnoosh Zamanlooy (Trans. Mach. Learn. Res., 2022)

A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; Ivan Dokmanic; David Belius (NeurIPS, 2023)

Generative Ornstein-Uhlenbeck Markets via Geometric Deep Learning. - Anastasis Kratsios; Cody B. Hyndman (CoRR, 2023)

A Transfer Principle: Universal Approximators Between Metric Spaces From Euclidean Universal Approximators. - Anastasis Kratsios; Chong Liu; Matti Lassas; Maarten V. de Hoop; Ivan Dokmanic (CoRR, 2023)

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing. - Xuwei Yang; Anastasis Kratsios; Florian Krach; Matheus R. Grasselli; Aurélien Lucchi (CoRR, 2023)

Deep Kalman Filters Can Filter. - Blanka Hovart; Anastasis Kratsios; Yannick Limmer; Xuwei Yang (CoRR, 2023)

Instance-Dependent Generalization Bounds via Optimal Transport. - Songyan Hou; Parnian Kassraie; Anastasis Kratsios; Andreas Krause 001; Jonas Rothfuss (J. Mach. Learn. Res., 2023)

Small Transformers Compute Universal Metric Embeddings. - Anastasis Kratsios; Valentin Debarnot; Ivan Dokmanic (J. Mach. Learn. Res., 2023)

Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices. - Calypso Herrera; Florian Krach; Anastasis Kratsios; Pierre Ruyssen; Josef Teichmann (Trans. Mach. Learn. Res., 2023)

Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios (ICLR, 2024)

Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; David Belius (ICML, 2024)

A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; David Belius (NeurIPS, 2024)

Energy-Guided Continuous Entropic Barycenter Estimation for General Costs. - Alexander Kolesov; Petr Mokrov; Igor Udovichenko; Milena Gazdieva; Gudmund Pammer; Anastasis Kratsios; Evgeny Burnaev; Aleksandr Korotin (NeurIPS, 2024)

Breaking the Curse of Dimensionality with Distributed Neural Computation. - Haitz Sáez de Ocáriz Borde; Takashi Furuya; Anastasis Kratsios; Marc T. Law (CoRR, 2024)

Digital Computers Break the Curse of Dimensionality: Adaptive Bounds via Finite Geometry. - Anastasis Kratsios; A. Martina Neuman; Gudmund Pammer (CoRR, 2024)

Mixture of Experts Soften the Curse of Dimensionality in Operator Learning. - Anastasis Kratsios; Takashi Furuya; Jose Antonio Lara Benitez; Matti Lassas; Maarten V. de Hoop (CoRR, 2024)

Reality Only Happens Once: Single-Path Generalization Bounds for Transformers. - Yannick Limmer; Anastasis Kratsios; Xuwei Yang; Raeid Saqur; Blanka Horvath (CoRR, 2024)

Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach. - Reza Arabpour; John Armstrong; Luca Galimberti; Anastasis Kratsios; Giulia Livieri (CoRR, 2024)

Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models. - Raeid Saqur; Anastasis Kratsios; Florian Krach; Yannick Limmer; Jacob-Junqi Tian; John Willes; Blanka Horvath; Frank Rudzicz (CoRR, 2024)

Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity. - Ruiyang Hong; Anastasis Kratsios (CoRR, 2024)

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization. - Mikhail Persiianov; Arip Asadulaev; Nikita Andreev; Nikita Starodubcev; Dmitry Baranchuk; Anastasis Kratsios; Evgeny Burnaev; Alexander Korotin (CoRR, 2024)

Simultaneously Solving FBSDEs with Neural Operators of Logarithmic Depth, Constant Width, and Sub-Linear Rank. - Takashi Furuya; Anastasis Kratsios (CoRR, 2024)

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning. - Haitz Sáez de Ocáriz Borde; Artem Lukoianov; Anastasis Kratsios; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2024)

Neural Operators Can Play Dynamic Stackelberg Games. - Guillermo Alvarez; Ibrahim Ekren; Anastasis Kratsios; Xuwei Yang (CoRR, 2024)

Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation. - Jose Antonio Lara Benitez; Takashi Furuya; Florian Faucher; Anastasis Kratsios; Xavier Tricoche; Maarten V. de Hoop (J. Comput. Phys., 2024)

Capacity bounds for hyperbolic neural network representations of latent tree structures. - Anastasis Kratsios; Ruiyang Hong; Haitz Sáez de Ocáriz Borde (Neural Networks, 2024)

Neural Spacetimes for DAG Representation Learning. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Marc T. Law; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Filtered not Mixed: Filtering-Based Online Gating for Mixture of Large Language Models. - Raeid Saqur; Anastasis Kratsios; Florian Krach; Yannick Limmer; Blanka Horvath; Frank Rudzicz (ICLR, 2025)

Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts. - Anastasis Kratsios; Haitz Sáez de Ocáriz Borde; Takashi Furuya; Marc T. Law (Trans. Mach. Learn. Res., 2025)

Is In-Context Universality Enough? MLPs are Also Universal In-Context. - Anastasis Kratsios; Takashi Furuya (CoRR, 2025)

Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent Learning Rate Constraints. - Kyle Sung; Anastasis Kratsios; Noah Forman (CoRR, 2025)

Keep It Light! Simplifying Image Clustering Via Text-Free Adapters. - Yicen Li; Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Paul D. McNicholas (CoRR, 2025)

Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement. - Anastasis Kratsios; Xiaofei Shi; Qiang Sun; Zhanhao Zhang (CoRR, 2025)

Kolmogorov-Arnold Networks: Approximation and Learning Guarantees for Functions and their Derivatives. - Anastasis Kratsios; Takashi Furuya (CoRR, 2025)

Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders. - Xuwei Yang; Fatemeh Tavakoli; David B. Emerson; Anastasis Kratsios (CoRR, 2025)

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters. - Anastasis Kratsios; Tin Sum Cheng; Aurélien Lucchi; Haitz Sáez de Ocáriz Borde (CoRR, 2025)

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs. - Reza Arabpour; Haitz Sáez de Ocáriz Borde; Anastasis Kratsios (CoRR, 2025)

Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs. - Anastasis Kratsios; Ariel Neufeld; Philipp Schmocker (CoRR, 2025)