The Neural Information Processing Systems Foundation (NeurIPS) purpose is to foster the exchange of research advances in AI and Machine Learning.

Ten papers coauthored by members of the Department of Computing have been accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

NeurIPS is one of the top conferences worldwide that brings together researchers in artificial intelligence, machine learning, neuroscience, statistics, optimization, computer vision, natural language processing.

- Z. Liu, R. Peach, P. A. Mediano, M.o Barahona; Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions

- M. Wicker, V. Piratla, A. Weller; Certification of Distributional Individual Fairness.
 
- J. Heo, V. Piratla, M. Wicker, A. Weller; Use Peturbations when Learning from Explanations.

- T. Schröder, Z. Ou, J. Ning Lim, Y. Li, S. J. Vollmer, A. Duncan; Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
 
- A. Robert, C. Pike-Burke &  A. A. Faisal; Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
 
- G. Kaissis, A. Ziller, S. Kolek, A. Riess, D. Rueckert; Optimal privacy guarantees against sub-optimal adversaries in differentially private machine learning
 
- S. Zhang, J. S. Campos, C. W. Feldmann, D. Walz, F. Sandfort, M. Mathea, C. Tsay, R. Misener; Optimizing over trained GNNs via symmetry breaking
 
- D. Paccagnan, M. C. Campi, S. Garatti; The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
 
- S. Swaminathan, A. Dedieu, R. V. Raju, M. Shanahan, M. Lazaro-Gredilla, D. George; Schema-learning and rebinding as mechanisms of in-context learning and emergence
 
- F. R. Ward, F. Toni, F. Belardinelli, T. Everitt; Honesty Is the Best Policy: Defining and Mitigating AI Deception