3 papers
cs.IT2025
The Conditional Regret-Capacity Theorem for Batch Universal Prediction
Marco Bondaschi, Michael Gastpar
We derive a conditional version of the classical regret-capacity theorem. This result can be used in universal prediction to find lower bounds on the minimal batch regret, which is…
cs.LG2025
What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains
Chanakya Ekbote, Marco Bondaschi, Nived Rajaraman +4
In-context learning (ICL) is a hallmark capability of transformers, through which trained models learn to adapt to new tasks by leveraging information from the input context. Prior…
cs.LG2024
Batch Normalization Decomposed
Ido Nachum, Marco Bondaschi, Michael Gastpar +1
\emph{Batch normalization} is a successful building block of neural network architectures. Yet, it is not well understood. A neural network layer with batch normalization comprises…