Basically every time I’ve shied away from a solution because it feels like cheating, or like it doesn’t count / address the real spirit of the problem, I’ve regretted it. Often it turns out it really doesn’t count, but knowing exactly why (and working on the problem with no holds barred) had been really important for me.
The most important case was dismissing imitation learning back in 2012-2014, together with basically giving up outright on all ML approaches, which I only recognized as a problem when I was writing up why those approaches were doomed more carefully and why imitation learning was a non-solution.
What is the main mistake you’ve made in your research, that you were wrong about?
Positive framing: what’s been the biggest learning moment in the course of your work?
Basically every time I’ve shied away from a solution because it feels like cheating, or like it doesn’t count / address the real spirit of the problem, I’ve regretted it. Often it turns out it really doesn’t count, but knowing exactly why (and working on the problem with no holds barred) had been really important for me.
The most important case was dismissing imitation learning back in 2012-2014, together with basically giving up outright on all ML approaches, which I only recognized as a problem when I was writing up why those approaches were doomed more carefully and why imitation learning was a non-solution.