activity
20172026
most citedOn the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks

55 citations · 57 across the 11 of their papers we have counts for

collaborators

17 papers

cs.LG2026

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

Tina Vartziotis, Rodopi Kosteli, Elli Vartziotis +5

The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems. Ho…

cs.CL2026

GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek

Yang Zhang, Mersin Konomi, Christos Xypolopoulos +6

Large Language Models (LLMs) are commonly trained on multilingual corpora that include Greek, yet reliable evaluation benchmarks for Greek-particularly those based on authentic, na…

cs.CL2026

CSE-UOI at SemEval-2026 Task 6: A Two-Stage Heterogeneous Ensemble with Deliberative Complexity Gating for Political Evasion Detection

Christos Tzouvaras, Konstantinos Skianis, Athanasios Voulodimos

This paper describes our system for SemEval-2026 Task 6, which classifies clarity of responses in political interviews into three categories: Clear Reply, Ambivalent, and Clear Non…

cs.CL2025

A Greek Government Decisions Dataset for Public-Sector Analysis and Insight

Giorgos Antoniou, Giorgos Filandrianos, Aggelos Vlachos +4

We introduce an open, machine-readable corpus of Greek government decisions sourced from the national transparency platform Diavgeia. The resource comprises 1 million decisions, fe…

cs.LG2025

KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations

Georgios Balanos, Evangelos Chasanis, Konstantinos Skianis +1

Retrieval-Augmented Generation (RAG) enhances language models by grounding responses in external information, yet explainability remains a critical challenge, particularly when ret…

cs.LG2025

On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets

Giannis Nikolentzos, Konstantinos Skianis

The Lipschitz constant of a neural network is connected to several important properties of the network such as its robustness and generalization. It is thus useful in many settings…