This talk is motivated by a visual neuroscience problem where individual subjects are asked to identify an oddball target image among many distractors, but are not told the nature of the oddball and distractors images. The key feature here is that the oddball and distractor images are unknown a priori and have to be learnt along the way. We propose an information theoretic lower bound on the expected search time, and provide an asymptotically optimal strategy, where the asymptotics is as the probability of erroneous detection vanishes. Such problems are of interest to neuroscientists that work on quantifying the “perceptual distance” between objects in order to understand how objects are represented in our brains. I will briefly describe the connection at the beginning of the talk. This is joint work with Nidhin Koshy Vaidhiyan
De Sidharth Jaggi
De Maxim Raginsky
De David Woodruff
De Ankit Garg
De Ran Gelles
De Iordanis Kerenidis
De Sidharth Jaggi
De Giacomo Como
De Gérard Ben Arous
De Giulio Biroli
De Giulio Biroli
De Gérard Ben Arous
