4 papers
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…
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…
Weak-to-Strong Generalization Even in Random Feature Networks, Provably
Marko Medvedev, Kaifeng Lyu, Dingli Yu +3
Weak-to-Strong Generalization (Burns et al., 2024) is the phenomenon whereby a strong student, say GPT-4, learns a task from a weak teacher, say GPT-2, and ends up significantly ou…
Shift is Good: Mismatched Data Mixing Improves Test Performance
Marko Medvedev, Kaifeng Lyu, Zhiyuan Li +1
We consider training and testing on mixture distributions with different training and test proportions. We show that in many settings, and in some sense generically, distribution s…