Performance of adaptive modulation techniques in the UMTS system. - Francesco Alesiani; Ezio Biglieri; Giorgio Taricco; Emanuele Viterbo (GLOBECOM, 2001)

Differential space-time CDMA with turbo decoding. - Francesco Alesiani; Alberto Tarable (GLOBECOM, 2003)

Opportunistic solution-space reduction techniques for reducing the time complexity of Dynamic Speed Control with microsimulation on motorways. - Konstantinos Gkiotsalitis; Francesco Alesiani (ITSC, 2014)

Optimization of Charging Stops for Fleet of Electric Vehicles: A Genetic Approach. - Francesco Alesiani; Nitin Maslekar (IEEE Intell. Transp. Syst. Mag., 2014)

Drift3Flow: Freeway-Incident Prediction Using Real-Time Learning. - Luís Moreira-Matias; Francesco Alesiani (ITSC, 2015)

Locally Growing Rapid Tree (LGRT) motion planning for autonomous driving. - Francesco Alesiani; Chairit Wuthishuwong (ITSC, 2016)

Remote Testimony: How to Trust an Autonomous Vehicle. - Francesco Alesiani; Sebastian Gajek (VTC Spring, 2016)

Reinforcement Learning-Based Bus Holding for High-Frequency Services. - Francesco Alesiani; Konstantinos Gkiotsalitis (ITSC, 2018)

Reliable bus dispatching times by coupling Monte Carlo evaluations with a Genetic Algorithm. - Konstantinos Gkiotsalitis; Francesco Alesiani (ITSC, 2018)

On Learning From Inaccurate and Incomplete Traffic Flow Data. - Francesco Alesiani; Luís Moreira-Matias; Mahsa Faizrahnemoon (IEEE Trans. Intell. Transp. Syst., 2018)

Efficient and Scalable Multi-Task Regression on Massive Number of Tasks. - Xiao He; Francesco Alesiani; Ammar Shaker (AAAI, 2019)

Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications. - Shujian Yu; Ammar Shaker; Francesco Alesiani; José C. Príncipe (IJCAI, 2020)

Towards Interpretable Multi-task Learning Using Bilevel Programming. - Francesco Alesiani; Shujian Yu; Ammar Shaker; Wenzhe Yin (ECML/PKDD (2), 2020)

Learning an Interpretable Graph Structure in Multi-Task Learning. - Shujian Yu; Francesco Alesiani; Ammar Shaker; Wenzhe Yin (CoRR, 2020)

Measuring Dependence with Matrix-based Entropy Functional. - Shujian Yu; Francesco Alesiani; Xi Yu; Robert Jenssen; José C. Príncipe (AAAI, 2021)

Reinforcement Learning for Route Optimization with Robustness Guarantees. - Tobias Jacobs; Francesco Alesiani; Gülcin Ermis (IJCAI, 2021)

Bilevel Continual Learning. - Ammar Shaker; Francesco Alesiani; Shujian Yu; Wenzhe Yin (IJCNN, 2021)

Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks. - Gülcin Ermis; Francesco Alesiani; Konstantinos Gkiotsalitis (ITSC, 2021)

Modular-Relatedness for Continual Learning. - Ammar Shaker; Francesco Alesiani; Shujian Yu (IDA, 2022)

PDEBench: An Extensive Benchmark for Scientific Machine Learning. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (NeurIPS, 2022)

Principle of relevant information for graph sparsification. - Shujian Yu; Francesco Alesiani; Wenzhe Yin; Robert Jenssen; José C. Príncipe (UAI, 2022)

Constrained Clustering for the Capacitated Vehicle Routing Problem (CC-CVRP). - Francesco Alesiani; Gülcin Ermis; Konstantinos Gkiotsalitis (Appl. Artif. Intell., 2022)

Human-Centric Research for NLP: Towards a Definition and Guiding Questions. - Bhushan Kotnis; Kiril Gashteovski; Julia Gastinger; Giuseppe Serra 002; Francesco Alesiani; Timo Sztyler; Ammar Shaker; Na Gong; Carolin Lawrence; Zhao Xu 001 (CoRR, 2022)

PDEBench Pretrained Models. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming. - Francesco Alesiani (AAAI, 2023)

Learning Neural PDE Solvers with Parameter-Guided Channel Attention. - Makoto Takamoto; Francesco Alesiani; Mathias Niepert (ICML, 2023)

Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling. - Henrik Christiansen; Federico Errica; Francesco Alesiani (CoRR, 2023)

Continual Invariant Risk Minimization. - Francesco Alesiani; Shujian Yu; Mathias Niepert (CoRR, 2023)

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Takashi Maruyama; Mathias Niepert; Francesco Alesiani (CoRR, 2023)

Gated information bottleneck for generalization in sequential environments. - Francesco Alesiani; Shujian Yu; Xi Yu (Knowl. Inf. Syst., 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Pretrained Models. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Viktor Zaverkin; Francesco Alesiani; Takashi Maruyama; Federico Errica; Henrik Christiansen; Makoto Takamoto; Nicolas Weber; Mathias Niepert (NeurIPS, 2024)

Graph Reasoning Networks. - Markus Zopf; Francesco Alesiani (CoRR, 2024)

Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad. - Jonathan Warrell; Francesco Alesiani; Cameron Smith; Anja Mösch; Martin Renqiang Min (CoRR, 2024)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2024)

Adaptive Width Neural Networks. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Mathias Niepert; Francesco Alesiani (CoRR, 2025)

Geometric Kolmogorov-Arnold Superposition Theorem. - Francesco Alesiani; Takashi Maruyama; Henrik Christiansen; Viktor Zaverkin (CoRR, 2025)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations. - Henrik Christiansen; Takashi Maruyama; Federico Errica; Viktor Zaverkin; Makoto Takamoto; Francesco Alesiani (CoRR, 2025)

Variational Kolmogorov-Arnold Network. - Francesco Alesiani; Henrik Christiansen; Federico Errica (CoRR, 2025)