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 (AISTATS) 11.

Lewi, J., Butera, R. & Paninski, L. (2009). Sequential optimal design of neurophysiology experiments. Neural Computation 21: 619-687.

Lewi, J., Schneider, D., Woolley, S. & Paninski, L. (2010). Automating the design of informative sequences of sensory stimuli. In press, Journal of Computational Neuroscience (special issue on methods of information theory in neuroscience research).

Liam Paninski's research