1 citations · 1 across the 5 of their papers we have counts for
6 papers
Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation
Haruka Tanzawa, Ayaka Sakata
We study high-dimensional LASSO under differential privacy via objective perturbation with heterogeneous covariate scales. In practical scenarios, covariates often exhibit diverse…
Privacy-Accuracy Trade-offs in High-Dimensional LASSO under Perturbation Mechanisms
Ayaka Sakata, Haruka Tanzawa
We study privacy-preserving sparse linear regression in the high-dimensional regime, focusing on the LASSO estimator. We analyze two widely used mechanisms for differential privacy…
Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
We consider sparse signal reconstruction via minimization of the smoothly clipped absolute deviation (SCAD) penalty, and develop one-step replica-symmetry-breaking (1RSB) extension…
High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator
Yuma Ichikawa, Shuhei Kashiwamura, Ayaka Sakata
Quantized neural network training optimizes a discrete, non-differentiable objective. The straight-through estimator (STE) enables backpropagation through surrogate gradients and i…
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model
Kaito Takanami, Takashi Takahashi, Ayaka Sakata
Self-distillation (SD), a technique where a model improves itself using its own predictions, has attracted attention as a simple yet powerful approach in machine learning. Despite…
Effect of Weight Quantization on Learning Models by Typical Case Analysis
Shuhei Kashiwamura, Ayaka Sakata, Masaaki Imaizumi
This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly in…