Feedback of ICML

"I think the paper is techincally sound, but I think that it is quite narrow in
scope, and am not sure it will be of great general interest. There are only few
and unsurprising conclusions, and moreover the results seem to indicate that
there is an interacation between IN/OUT and the specific FS algorithms used.
Focussing so strongly on toy data, is also a considerable worry. I recommend
rejection. "

It seems that this year is not my "lucky" year. This is the same situation for my intern applications. Getting so many interviews(7 interviews), but none of them makes an offer.

Anyway, just keep going!

Where to Go?

Recently, I seems to lose the enthusiasm of research. No mood to read, to discuss, to work on experiments. I know this is not correct way to go, but I just couldn't help become anxious. I would rather there's a problem let me to just jump in.

I don't know whether this is related to the frustration of recent progress. Too slow.

To be frank, I really doubt current development of machine learning field. Too fast. Each year, in the top level conference, there are lots of publications, but very few of them worth reading. Lots of them are just rubbish. Is this common in other fields?

I am wondering which direction should be a correct way to go. Searching in blind is really difficult:(

KDD submisssion is done

Nitin and I coauthored one paper talking about finding influentials in blogosphere. It's an interesting problem. the method seems a little ad hoc. But we finally made it.

Through a long distance communication (that's the wonder of Web), Nitin is working in India while Dr.Liu and I were working in the united states. The final version seems much better. But whether or not this work can be accepted is really depending on our luck. Part of my concern the technical part is really subjective.

I don't know whether the reviewer is obsessed with cross validation.

Let's see.

Done with the ICML paper

OK. Payam and I just finished the ICML paper. It's good. I believe it should be accepted unless we are really unlucky.

Anyway, it's a good experience to work with Payam. I believe our cooperation makes the task much easier and efficient.

Hope we can get good news soon.

Need to rush for my KDD paper now:)

Just finished the journal paper

I've just finished the journal paper for TKDD.
This paper really takes a while to fill in. I even got puke for rewriting it.

I guess I've looked through it at least 15 times. I really cannot bear to read it any more.

I am wondering, to do research, should I put more effort to polish a paper or to think about the idea?

I really hate spending so much time on writing the paper. i think it's not worthwhile.

The ridiculous thing about current stage of machine learning and data mining is that all these papers tend to foster UNNECESSARY formula to make paper more easy to accept.

That's not the goal for research. i would like more to motivate the problem well and present some simple, intuitive, sensible and working methods!!