14 citations · 46 across the 25 of their papers we have counts for
26 papers
Dual Riemannian Newton Method on Statistical Manifolds
Derun Zhou, Keisuke Yano, Mahito Sugiyama
In probabilistic modeling, parameter estimation is commonly formulated as a minimization problem on a parameter manifold. Optimization in such spaces requires geometry-aware method…
Linear Mode Connectivity in Differentiable Tree Ensembles
Ryuichi Kanoh, Mahito Sugiyama
Linear Mode Connectivity (LMC) refers to the phenomenon that performance remains consistent for linearly interpolated models in the parameter space. For independently optimized mod…
StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds
Prasad Cheema, Mahito Sugiyama
Data augmentation is an area of research which has seen active development in many machine learning fields, such as in image-based learning models, reinforcement learning for self…
Molecular Graph Generation by Decomposition and Reassembling
Masatsugu Yamada, Mahito Sugiyama
Designing molecular structures with desired chemical properties is an essential task in drug discovery and material design. However, finding molecules with the optimized desired pr…
Many-body Approximation for Non-negative Tensors
Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara
We present an alternative approach to decompose non-negative tensors, called many-body approximation. Traditional decomposition methods assume low-rankness in the representation, r…
Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel
Ryuichi Kanoh, Mahito Sugiyama
A soft tree is an actively studied variant of a decision tree that updates splitting rules using the gradient method. Although soft trees can take various architectures, their impa…
Fast Rank-1 NMF for Missing Data with KL Divergence
Kazu Ghalamkari, Mahito Sugiyama
We propose a fast non-gradient-based method of rank-1 non-negative matrix factorization (NMF) for missing data, called A1GM, that minimizes the KL divergence from an input matrix t…
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
Ryuichi Kanoh, Mahito Sugiyama
In practical situations, the tree ensemble is one of the most popular models along with neural networks. A soft tree is a variant of a decision tree. Instead of using a greedy meth…
Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change
Ryuichi Kanoh, Mahito Sugiyama
A multiplicative constant scaling factor is often applied to the model output to adjust the dynamics of neural network parameters. This has been used as one of the key intervention…
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Kazu Ghalamkari, Mahito Sugiyama
We present an efficient low-rank approximation algorithm for non-negative tensors. The algorithm is derived from our two findings: First, we show that rank-1 approximation for tens…
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
Simon Luo, Feng Zhou, Lamiae Azizi +1
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower…
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
Kazu Ghalamkari, Mahito Sugiyama
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix. Our key insight is…
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective
Prasad Cheema, Mahito Sugiyama
The appearance of the double-descent risk phenomenon has received growing interest in the machine learning and statistics community, as it challenges well-understood notions behind…
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
Simon Luo, Lamiae Azizi, Mahito Sugiyama
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the log-linear model equipped wit…
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
Simon Luo, Mahito Sugiyama
Hierarchical probabilistic models are able to use a large number of parameters to create a model with a high representation power. However, it is well known that increasing the num…
Learning Graph Representation via Formal Concept Analysis
Yuka Yoneda, Mahito Sugiyama, Takashi Washio
We present a novel method that can learn a graph representation from multivariate data. In our representation, each node represents a cluster of data points and each edge represent…
Transductive Boltzmann Machines
Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara
We present transductive Boltzmann machines (TBMs), which firstly achieve transductive learning of the Gibbs distribution. While exact learning of the Gibbs distribution is impossib…
Legendre Decomposition for Tensors
Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda
We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks…
Finding Statistically Significant Interactions between Continuous Features
Mahito Sugiyama, Karsten Borgwardt
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare…
Tensor Balancing on Statistical Manifold
Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda
We solve tensor balancing, rescaling an Nth order nonnegative tensor by multiplying N tensors of order N - 1 so that every fiber sums to one. This generalizes a fundamental process…
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
Shinya Suzumura, Kazuya Nakagawa, Mahito Sugiyama +2
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the…
Information Decomposition on Structured Space
Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda
We build information geometry for a partially ordered set of variables and define the orthogonal decomposition of information theoretic quantities. The natural connection between i…
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
Felipe Llinares López, Mahito Sugiyama, Laetitia Papaxanthos +1
We present a novel algorithm, Westfall-Young light, for detecting patterns, such as itemsets and subgraphs, which are statistically significantly enriched in one of two classes. Ou…
Identifying Higher-order Combinations of Binary Features
Felipe Llinares, Mahito Sugiyama, Karsten M. Borgwardt
Finding statistically significant interactions between binary variables is computationally and statistically challenging in high-dimensional settings, due to the combinatorial expl…