111 citations · 283 across the 44 of their papers we have counts for
9 papers · 1 filter
Adaptive Normalized Gradient Method for -smooth Problems: Estimating Problem Parameters as a Region
Rikiya Okazaki, Shotaro Yagishita, Hideitsu Hino
We propose a parameter-free and efficient gradient descent algorithm for -smooth optimization. This notion of smoothness, introduced by Zhang et al. (2020), allows the s…
Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels
Hideaki Ishibashi, Takahiro Kawashima, Hideitsu Hino
Determinantal point processes (DPPs) model subset selection probability with repulsion, but many applications also exhibit attraction arising from pairwise relations or shared late…
Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions
Takahiro Kawashima, Hideitsu Hino
Determinantal point processes (DPPs) on a finite set are probability models that exhibit repulsion where inter-item similarities are encoded by a positive definite kernel matrix. S…
Knowledge Distillation under Teacher Misspecification: An Order-Parameter Analysis of the Gap between Teacher Mimicry and Task Performance
Kazuyuki Hara, Hideitsu Hino
Knowledge distillation trains a small student model to reproduce the outputs of a large teacher model, and its progress is typically monitored through the teacher--student discrepa…
Determinantal Point Process Approximation under Positive and Negative Dependence
So Anzai, Hideitsu Hino
Determinantal point processes (DPPs) are widely used as probabilistic models for diverse random subsets, but their approximation error under model misspecification has not been ful…
Projective Maximum Entropy: Universality and Acceptance-Region Calibration
Hideitsu Hino
Maximum-entropy reference distributions are usually constructed on the normalized probability simplex. This formulation is less natural for unnormalized statistical models, in whic…
LIG: Layer-wise Integrated Gradients for Within-Layer Flow Analysis in Transformers
Eight Suzuki, Hideitsu Hino, Noboru Murata
Transformers achieve strong performance, but their internal computations remain opaque. We view each Transformer layer as a dynamic graph whose nodes are token representations and…
From DPPs to -DPPs: identifiability analysis via spectral decomposition
Hideitsu Hino, Keisuke Yano
We study the geometry of determinantal point processes (DPPs) through the spectral decomposition . The spectrum governs the cardinality distribution via elementar…
Sobolev--Ricci Curvature
Kyoichi Iwasaki, Tam Le, Hideitsu Hino
Ricci curvature is a fundamental concept in differential geometry for encoding local geometric structure, and its graph-based analogues have recently gained prominence as practical…