74 citations · 120 across the 41 of their papers we have counts for
11 papers · 1 filter
One model to solve them all: 2BSDE families via neural operators
Takashi Furuya, Anastasis Kratsios, Dylan Possamaï +1
We introduce a mild generative variant of the classical neural operator model, which leverages Kolmogorov--Arnold networks to solve infinite families of second-order backward stoch…
Autonomous-Flow-Based Generation
Hossein Rouhvarzi, Anastasis Kratsios
We show that using autonomous-flow-based generation, one can universally approximate orientation-preserving diffeomorphisms defined on the cube by Neural ODEs with rate $\mathcal{O…
Quantifying The Limits of AI Reasoning: Systematic Neural Network Representations of Algorithms
Anastasis Kratsios, Dennis Zvigelsky, Bradd Hart
A main open question in contemporary AI research is quantifying the forms of reasoning neural networks can perform when perfectly trained. This paper answers this by interpreting r…
Learning from one graph: transductive learning guarantees via the geometry of small random worlds
Nils Detering, Luca Galimberti, Anastasis Kratsios +2
Since their introduction by Kipf and Welling in , a primary use of graph convolutional networks is transductive node classification, where missing labels are inferred within…
Neural Operators Which Certifiably Simultaneously Solve Infinitely Many Convex Programs using Logarithmic Rank and Linear Complexity
Anastasis Kratsios, Ariel Neufeld, Philipp Schmocker
Neural operators (NOs) are a class of deep learning models designed to simultaneously solve infinitely many related problems by casting them into an infinite-dimensional space, whe…
LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs
Reza Arabpour, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios
Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates. However, their widespread adoption remains limit…
Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement
Anastasis Kratsios, Xiaofei Shi, Qiang Sun +1
We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial fric…
Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
Anastasis Kratsios, Bum Jun Kim, Takashi Furuya
Inspired by the Kolmogorov-Arnold superposition theorem, Kolmogorov-Arnold Networks (KANs) have recently emerged as an improved backbone for most deep learning frameworks, promisin…
Is In-Context Universality Enough? MLPs are Also Universal In-Context
Anastasis Kratsios, Takashi Furuya
The success of transformers is often linked to their ability to perform in-context learning. Recent work shows that transformers are universal in context, capable of approximating…
Keep It Light! Simplifying Image Clustering Via Text-Free Adapters
Yicen Li, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios +1
In the era of pre-trained models, effective classification can often be achieved using simple linear probing or lightweight readout layers. In contrast, many competitive clustering…
Step by Step: Adaptive Gradient Descent for Training L-Lipschitz Neural Networks
Kyle Sung, Kholood Khalil, Noah Forman +2
We demonstrate that applying an eventual decay to the learning rate (LR) in empirical risk minimization (ERM), where the mean-squared-error loss is minimized using standard gradien…