Author name cluster

Koji Maruhashi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAAI Conference 2025 Conference Paper

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning

Shuyu Dong
Michele Sebag
Kento Uemura
Akito Fujii
Shuang Chang
Yusuke Koyanagi
Koji Maruhashi

Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DCILP. In the divide phase, the Markov blanket MB(Xi) of each variable Xi is identified, and causal learning subproblems associated with each MB(Xi) are independently addressed in parallel. This approach benefits from a more favorable ratio between the number of data samples and the number of variables considered. In counterpart, it can be adversely affected by the presence of hidden confounders, as variables external to MB(Xi) might influence those within it. The reconciliation of the local causal graphs generated during the divide phase is a challenging combinatorial optimization problem, especially in large-scale applications. The main novelty of DCILP is an original formulation of this reconciliation as an integer linear programming (ILP) problem, which can be delegated and efficiently handled by an ILP solver. Through experiments on medium to large scale graphs, and comparisons with state-of-the-art methods, DCILP demonstrates significant improvements in terms of computational complexity, while preserving the learning accuracy on real-world problem and suffering at most a slight loss of accuracy on synthetic problems.

PDF Details DOI

ICLR Conference 2023 Conference Paper

Automated Data Augmentations for Graph Classification

Youzhi Luo
Michael McThrow
Wing Yee Au 0002
Tao Komikado
Kanji Uchino
Koji Maruhashi
Shuiwang Ji

Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification. Instead of using uniform transformations as in existing studies, GraphAug uses an automated augmentation model to avoid compromising critical label-related information of the graph, thereby producing label-invariant augmentations at most times. To ensure label-invariance, we develop a training method based on reinforcement learning to maximize an estimated label-invariance probability. Experiments show that GraphAug outperforms previous graph augmentation methods on various graph classification tasks.

Details

ICML Conference 2022 Conference Paper

Generating 3D Molecules for Target Protein Binding

Meng Liu 0015
Youzhi Luo
Kanji Uchino
Koji Maruhashi
Shuiwang Ji

A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind to given proteins by placing atoms of specific types and locations to the given binding site one by one. In particular, at each step, we first employ a 3D graph neural network to obtain geometry-aware and chemically informative representations from the intermediate contextual information. Such context includes the given binding site and atoms placed in the previous steps. Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system. Finally, to place a new atom, we generate its atom type and relative location w. r. t. the constructed local coordinate system via a flow model. We also consider generating the variables of interest sequentially to capture the underlying dependencies among them. Experiments demonstrate that our GraphBP is effective to generate 3D molecules with binding ability to target protein binding sites. Our implementation is available at https: //github. com/divelab/GraphBP.

Details

AAAI Conference 2018 Conference Paper

Learning Multi-Way Relations via Tensor Decomposition With Neural Networks

Koji Maruhashi
Masaru Todoriki
Takuya Ohwa
Keisuke Goto
Yu Hasegawa
Hiroya Inakoshi
Hirokazu Anai

How can we classify multi-way data such as network trafﬁc logs with multi-way relations between source IPs, destination IPs, and ports? Multi-way data can be represented as a tensor, and there have been several studies on classiﬁcation of tensors to date. One critical issue in the classiﬁcation of multi-way relations is how to extract important features for classiﬁcation when objects in different multi-way data, i. e. , in different tensors, are not necessarily in correspondence. In such situations, we aim to extract features that do not depend on how we allocate indices to an object such as a speciﬁc source IP; we are interested in only the structures of the multi-way relations. However, this issue has not been considered in previous studies on classiﬁcation of multi-way data. We propose a novel method which can learn and classify multi-way data using neural networks. Our method leverages a novel type of tensor decomposition that utilizes a target core tensor expressing the important features whose indices are independent of those of the multi-way data. The target core tensor guides the tensor decomposition into more effective results and is optimized in a supervised manner. Our experiments on three different domains show that our method is highly accurate, especially on higher order data. It also enables us to interpret the classiﬁcation results along with the matrices calculated with the novel tensor decomposition.

PDF Details