Related publications:
- Emmerich M and Deutz A (2014), “Time Complexity and Zeros of the Hypervolume Indicator Gradient Field“, In EVOLVE – A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Vol. 500, pp. 169-193. Springer International Publishing.
- Sosa Hernandez VA, Schuetze O and Emmerich M (2014), “Hypervolume Maximization via Set Based Newton’s Method“, In EVOLVE – A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Vol. 288, pp. 15-28. Springer International Publishing.
- van Rijn S, Wang H, van Stein B and Bäck T (2017), “Algorithm configuration data mining for CMA evolution strategies“, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 737-744. ACM.
- Holanda P, Raasveldt M, and Kersten M (2017), “Don’t Hold My UDFs Hostage – Exporting UDFs For Debugging Purposes“, SBBD. [Source-Code].
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Rassveldt M, Holanda P, Mühleisen H. and Manegold S (2018), “Deep Integration of Machine Learning Into Column Stores“, EDBT. [Source-Code].
- Holanda P (2018), “Progressive Indices – Indexing Without Prejudice“, Ph.D. Workshop@VLDB. [Source-Code].
- Raasveldt M, Holanda P, Gubner T, Mühleisen H (2018), “Fair Benchmarking Considered Difficult: Common Pitfalls In Database Performance Testing.“, DBTest@SIGMOD. [Source-Code].
- Holanda P, Nerone M, Almeida E, Manegold S (2018), “Cracking KD-Tree: The First Multidimensional Adaptive Indexing.“, EDDY@DATA. [Source-Code].
- Raasveldt M, Holanda P, Manegold S (2019), “devUDF: Increasing UDF development efficiency through IDE Integration. It works like a PyCharm!“, Demo@EDBT. [Source-Code].
- Holanda P, Mühleisen H (2019). “Relational Queries with a Tensor Processing Unit“, DAMON@ SIGMOD. [Source-Code].
- Holanda P, Raasveldt M (2019). “Dissecting DuckDB: The internals of the “SQLite for Analytics””. Tutorial@SBBD. [Source-Code].
- Holanda P, Raasveldt M, Manegold S, Mühleisen H (2020). “Progressive Indexes: Indexing for Interactive Data Analysis“. VLDB. [Source-Code].
- van Rijn S and Schmitt S (2020). “MF2: A Collection of Multi-Fidelity Benchmark Functions in Python“, In JOSS – the Journal of Open Source Software. [GitHub repository].