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20242026
most citedBenchmarking Counterfactual Image Generation

5 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2026

Incremental Recommendation via Causal Models

Athanasios Vlontzos, David Gustafsson, Michael O'Riordan +1

Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no incremental value and displaces o…

cs.AI2026

From Training to Deployment: Post-Hoc Causal Feature Identification via Sensitivity Ratios

Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz +1

Given a model that is already trained, which features does it rely on causally versus spuriously? Existing methods require access to the training procedure and cannot answer this p…

cs.AI2026

Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners

Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz +1

Consider a model trained at a single hospital to predict patient recovery, where the measured feature bundles the patient's true health signal () with a systematic artefact…

cs.CV2025

Causally Steered Diffusion for Automated Video Counterfactual Generation

Nikos Spyrou, Athanasios Vlontzos, Paraskevas Pegios +5

Adapting text-to-image (T2I) latent diffusion models (LDMs) to video editing has shown strong visual fidelity and controllability, but challenges remain in maintaining causal relat…

cs.CV2024★ 5 cited

Benchmarking Counterfactual Image Generation

Thomas Melistas, Nikos Spyrou, Nefeli Gkouti +5

Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in…