8 citations · 8 across the 11 of their papers we have counts for
4 papers · 1 filter
Pretrained transformer efficiently learns low-dimensional target functions in-context
Kazusato Oko, Yujin Song, Taiji Suzuki +1
Transformers can efficiently learn in-context from example demonstrations. Most existing theoretical analyses studied the in-context learning (ICL) ability of transformers for line…
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
Kazusato Oko, Yujin Song, Taiji Suzuki +1
We study the computational and sample complexity of learning a target function with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m…
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Jason D. Lee, Kazusato Oko, Taiji Suzuki +1
We study the problem of gradient descent learning of a single-index target function unde…
Flow matching achieves almost minimax optimal convergence
Kenji Fukumizu, Taiji Suzuki, Noboru Isobe +2
Flow matching (FM) has gained significant attention as a simulation-free generative model. Unlike diffusion models, which are based on stochastic differential equations, FM employs…