3 papers
cs.LG2026
Fast Adversarial Attacks with Gradient Prediction
Kamil Ciosek, Aleksandr V. Petrov, Nicolò Felicioni +1
Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even "fast" attacks such as FGSM remain throughpu…
cs.IR2025
From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections
Gustavo Penha, Aleksandr V. Petrov, Claudia Hauff +9
The Cranfield paradigm has long provided reliable, reproducible evaluation in ad hoc retrieval, and recent work has begun extending this framework to recommender systems. A recent…
cs.IR2025
LLMs for estimating positional bias in logged interaction data
Aleksandr V. Petrov, Michael Murtagh, Karthik Nagesh
Recommender and search systems commonly rely on Learning To Rank models trained on logged user interactions to order items by predicted relevance. However, such interaction data is…