Hi Marcus, Sorry for bothering you. I tried introduce myself on mailinglist by sending email to [email protected] <mailto:[email protected]>, but I didn’t see my post coming out. I am not sure what’s the problem.
Best Regards, Kungang (Karl) Zhang [email protected] > Begin forwarded message: > > From: Kungang Zhang <[email protected]> > Subject: Interest in contributing to mlpack > Date: February 5, 2019 at 10:57:35 CST > To: [email protected] > > 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]> > > > >
_______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
