6 citations · 6 across the 10 of their papers we have counts for
16 papers
On sample complexity for covariance estimation via the unadjusted Langevin algorithm
Shogo Nakakita
We establish sample complexity guarantees for estimating the covariance matrix of a strongly log-concave smooth distribution using the unadjusted Langevin algorithm (ULA). We quant…
Sparse estimation for the drift of high-dimensional Ornstein--Uhlenbeck processes with i.i.d. paths
Shogo Nakakita
We study sparsity-regularized maximum likelihood estimation for the drift parameter of high-dimensional non-stationary Ornstein--Uhlenbeck processes given repeated measurements of…
Improved generalization bounds for binary linear classification via isoperimetry
Shogo Nakakita
We examine the concentration of uniform generalization errors around their expectation in binary linear classification problems via an isoperimetric argument. In particular, we est…
Federated Learning with Relative Fairness
Shogo Nakakita, Tatsuya Kaneko, Shinya Takamaeda-Yamazaki +1
This paper proposes a federated learning framework designed to achieve \textit{relative fairness} for clients. Traditional federated learning frameworks typically ensure absolute f…
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution
Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi
We consider a variant of the stochastic gradient descent (SGD) with a random learning rate and reveal its convergence properties. SGD is a widely used stochastic optimization algor…
Dimension-free uniform concentration bound for logistic regression
Shogo Nakakita
We provide a novel dimension-free uniform concentration bound for the empirical risk function of constrained logistic regression. Our bound yields a milder sufficient condition for…