◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Alexis Lazanas

3 papers hereh-index 578 citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author2

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.LG1
  • q-fin.ST1

identity via Semantic Scholar / OpenAlex

most citedBreaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance

18 citations · 18 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2026★ 18 cited

Breaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance

Alexis Lazanas, Georgios Kampouropoulos

Supervised learning models in the predictive maintenance field are regularly trained on highly imbalanced industrial datasets: machine failures occur rarely but have a disproportio…

q-fin.ST2026

Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling

Alexis Lazanas, Spyridon Karpouzis

The problem of time-series forecasting in non-stationary and complex environments is a challenging task in machine learning, especially with heterogeneous numerical and textual dat…

cs.AI2009

Performing Hybrid Recommendation in Intermodal Transportation-the FTMarket System's Recommendation Module

Alexis Lazanas

Diverse recommendation techniques have been already proposed and encapsulated into several e-business applications, aiming to perform a more accurate evaluation of the existing inf…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.