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cs.LG2026
Learning through Internalization
Nikolaos Tsilivis, Nirmit Joshi, Marko Medvedev +2
We study internalization processes, by which neural-network-based systems absorb an explicit computational procedure into their own weights, and how they facilitate learning. We in…
cs.LG2026
Positive Distribution Shift as a Framework for Understanding Tractable Learning
Marko Medvedev, Idan Attias, Elisabetta Cornacchia +3
We study a setting where the goal is to learn a target function f(x) with respect to a target distribution D(x), but training is done on i.i.d. samples from a different training di…