Hello, My name is Kungang Zhang, currently studying in PhD program in Northwestern University, Evanston, IL. My research including statistics, machine learning, optimization, and artificial intelligence. Currently, I am specifically interested in research in hyper-parameter tuning and efficient implementation of those algorithms. With more and more data and data stream, models is getting increasingly complex and optimizing them becomes very costly for a set of hyper-parameters. To find the best hyper-parameter is critical for good performance. This is an active field of research right now, but not many good and efficient implementations can be found out there. Cross-validation (or simple validation) is usually the to-go method, but too costly for large-scale model, limiting amount of data points, and online learning problems. Currently I am interested in implementing new algorithms to automate this tuning process, not only for categorical hyper-parameters, but also for continuous hyper-parameters. According to my research there are several methods but no definite answer which one is the best, so that implementing them in mlpack can help exploration of new ideas and new datasets and definitely improving the diversity in algorithms for hyper-parameter tuning.
This idea is related to my interest in reinforcement learning, because I got this idea from my interest in multi-arm bandit problem (a simple version of RL) and my last internship. It is kind of being proved working in real applications, but of course efficient implementation and new ideas are worth of more effort. I have reading mlpack mailing list for a while and think I can learn from and contribute to this community by doing this project (besides day-to-day interaction). I am considering applying GSoC 2019, even though there is no detailed project about hyper-parameter tuning in the idea list yet. Any advice on how to prepare ideas and proposals for this is very welcome. Also, I am currently interested in Reinforcement Learning. I also want to implement efficient algorithms for RL package and may be try some new ideas. Thank you very much! Best Regards, Kungang (Karl) Zhang [email protected] <mailto:[email protected]>
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