3 papers
stat.ML2026
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…
stat.ML2025
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…
stat.ML2025
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…