6 papers
Unified generalization analysis for physics informed neural networks
Yuka Hashimoto, Tomoharu Iwata
Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them useful for scientific problems.…
Relative Density Ratio Optimization for Stable and Statistically Consistent Model Alignment
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +4
Aligning language models with human preferences is essential for ensuring their safety and reliability. Although most existing approaches assume specific human preference models su…
A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization
Hideaki Kim, Tomoharu Iwata
The representer theorem is a cornerstone of kernel methods, which aim to estimate latent functions in reproducing kernel Hilbert spaces (RKHSs) in a nonparametric manner. Its signi…
Concept Unlearning in Large Language Models via Self-Constructed Knowledge Triplets
Tomoya Yamashita, Yuuki Yamanaka, Masanori Yamada +3
Machine Unlearning (MU) has recently attracted considerable attention as a solution to privacy and copyright issues in large language models (LLMs). Existing MU methods aim to remo…
KIE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
Hideaki Kim, Tomoharu Iwata, Akinori Fujino
Kernel method-based intensity estimators, formulated within reproducing kernel Hilbert spaces (RKHSs), and classical kernel intensity estimators (KIEs) have been among the most eas…
Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices
Yuka Hashimoto, Tomoharu Iwata
We propose deep Koopman-layered models with learnable parameters in the form of Toeplitz matrices for analyzing the transition of the dynamics of time-series data. The proposed mod…