Optimal sequential experimental design (active learning)
It is often expensive to run experiments. Thus we would like to
choose our experimental stimuli so that we can learn as much as
possible in just a few trials. In many cases, we can try to optimize
our stimuli online, adapting our experiments to take into account each
new observation. We have examined this problem both theoretically
(how much does this adaptive experimental approach help, in the long
run?) and computationally (is it possible to perform this optimization
in real time?), and many interesting questions remain open.
Paninski, L. (2005). Asymptotic
theory of information-theoretic experimental design. Neural
Computation 17: 1480-1507.
Lewi, J., Butera, R. & Paninski, L. (2007). Efficient active learning with
generalized linear models. Artificial Intelligence and Statistics
Lewi, J., Butera, R. & Paninski, L. (2009).
Sequential optimal design of neurophysiology
Neural Computation 21:
Lewi, J., Schneider, D., Woolley, S. & Paninski, L. (2010).
Automating the design of informative sequences of
In press, Journal of
Computational Neuroscience (special issue on methods of information
theory in neuroscience research).
Liam Paninski's research