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cs.IT2025
Asymptotically Minimax Regret by Bayes Mixtures
Jun'ichi Takeuchi, Andrew R. Barron
We study the problems of data compression, gambling and prediction of a sequence from an alphabet , in terms of regret and expected regret (redundancy)…
cs.IT2025
Sparse Superposition Codes with Binomial Dictionary are Capacity-Achieving with Maximum Likelihood Decoding
Yoshinari Takeishi, Jun'ichi Takeuchi
It is known that sparse superposition codes asymptotically achieve the channel capacity over the additive white Gaussian noise channel with both maximum likelihood decoding and eff…
cs.IT2024
Risk Bounds on MDL Estimators for Linear Regression Models with Application to Simple ReLU Neural Networks
Yoshinari Takeishi, Jun'ichi Takeuchi
To investigate the theoretical foundations of deep learning from the viewpoint of the minimum description length (MDL) principle, we analyse risk bounds of MDL estimators based on…