Our paper titled “How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design” (link on the publication page), co-authored with Ellen Vitercik, Travis Dick, Toumas Sandholm, Nina Balcan, and Carl Kingsford was accepted to STOC 2021. The acceptance rate for this year’s (virtual) STOC meeting was only 28%.
Our paper More Accurate Transcript Assembly via Parameter Advising co-authored with Kwanho Kim and Carl Kingsford, was featured in this months issues of the Journal for Computational Biology. This is the special issue of JCB with work from WCB@ICML which I mentioned previously. Link to the paper on the publications page.
The work that was done while Fiyinfoluwa Gbosibo was at CMU during the summer of 2017 which was published at ACM-BCB this week won the best student paper award. The paper titled “Practical Universal k-mer Sets for Minimizer Schemes” also has Carl and Guillaume as authors. Congrats Fiyin!
The full paper is available in the BCB proceedings and are open access.
I have been asked to give a talk at the Workshop on Automated Algorithm Design being held at the Toyota Technical Institute at Chicago from August 7th to the 9th. I will be speaking about our work related to applying parameter advising to reference-based transcript assembly (see Publications).
While in Chicago, I will also attend the Workshop on Learning-Based Algorithms also being held at TTIC from August 12th to the 14th.
Our recent work on universal k-mer sets has been accepted as a paper to The 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) in September, being held in Niagara Falls, NY. The work titled “Practical universal k-mer sets for minimizer schemes” is a collaboration with our summer iBRIC student from 2017 Fiyin Gbosibo, Carl Kingsford, and Guillaume Marçais. The preprint is now available (see Publications).