Publications

Shujian Yu, Francesco Alesiani (2022). Scalable, accurate and reliable measure of variable dependence and independence, and utilization of the measure to train a neural network.

Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, Jose C Principe (2022). Principle of Relevant Information for Graph Sparsification (Supp. Material).

Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, Jose C Principe (2022). Principle of relevant information for graph sparsification. Uncertainty in Artificial Intelligence.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert (2022). Pdebench: An extensive benchmark for scientific machine learning. Advances in Neural Information Processing Systems.

Ammar Shaker, Francesco Alesiani, Shujian Yu (2022). Modular-relatedness for continual learning. International Symposium on Intelligent Data Analysis.

Francesco Alesiani, Makoto Takamoto, Mathias Niepert (2022). HyperFNO: Improving the generalization behavior of Fourier Neural Operators. NeurIPS 2022 Workshop on Machine Learning and Physical Sciences.

Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger, Giuseppe Serra, Francesco Alesiani, Timo Sztyler, Ammar Shaker, Na Gong, Carolin Lawrence, Zhao Xu (2022). Human-centric research for nlp: Towards a definition and guiding questions. arXiv preprint arXiv:2207.04447.

Francesco Alesiani, Gulcin Ermis, Konstantinos Gkiotsalitis (2022). Constrained clustering for the capacitated vehicle routing problem (cc-cvrp). Applied artificial intelligence.

Francesco Alesiani (2022). BiGrad: Differentiating through bilevel optimization programming. The AAAI-22 Workshop on Adversarial Machine Learning and Beyond.

Francesco Alesiani, Shujian Yu (2021). Methods and systems for graph approximation.