Join HERCOLE Lab for two events at CIKM 2026
Meet us at the LLM4XAI workshop on November 8 and at our hypergraph counterfactual explanations poster on November 11.
Human-Explainable, Robust, and COllaborative LEarning.
At the HERCOLE Lab, our research mission is to advance the development of next-generation machine learning and artificial intelligence systems that are not only powerful, but also understandable, resilient, and decentralized.
We strive to make AI systems more interpretable and transparent to human users, enabling greater trust and collaboration. We design algorithms that are robust to adversarial attacks, ensuring reliability in real-world conditions.
Moreover, we design intelligent systems that can operate efficiently on edge devices, enabling local decision-making, enhancing privacy, and reducing reliance on centralized infrastructures.
Through interdisciplinary research and innovation, we aim to shape AI that is more human-centered, secure, and scalable.
Meet us at the LLM4XAI workshop on November 8 and at our hypergraph counterfactual explanations poster on November 11.
TRACER helps generative recommender systems forget unwanted concepts while preserving the quality of other recommendations.
Our paper introduces CF-HyperGNNExplainer, a method for explaining hypergraph neural network predictions through small, meaningful structural changes.