György, András

Degenerate Feedback Loops in Recommender Systems

Jiang, R., Chiappa, S., Lattimore, T., György, A., & Kohli, P. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Recommender Systems and TechniquesEthics and Social Impacts of AI

Cited by 232

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 47

Mitigating LLM Hallucinations via Conformal Abstention

Yadkori, Y. A., Kuzborskij, I., Stutz, D., György, A., Fisch, A., Doucet, A., Beloshapka, I., Weng, W.-H., Yang, Y.-Y., Szepesvári, C., et al. (2024) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 2

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence

György, A., Lattimore, T., Lazić, N., & Szepesvári, C. (2025) · arXiv

Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

Cited by 0

Frontier LLMs Still Struggle with Simple Reasoning Tasks

Malek, A., Ge, J., Lazic, N., Jin, C., György, A., & Szepesvári, C. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0