Author name cluster

Akihiro Kishimoto

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.

39 papers

2 author rows

IJCAI Conference 2023 Conference Paper

An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations

Achille Fokoue
Ibrahim Abdelaziz
Maxwell Crouse
Shajith Ikbal
Akihiro Kishimoto
Guilherme Lima
Ndivhuwo Makondo
Radu Marinescu

Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the names used in these statements and, as a result, the models are generally not transferable from one domain to another. The size of these representations and whether to include the whole theory or part of it are other important decisions that affect the performance of these approaches as well as their runtime efficiency. In this paper, we present NIAGRA; an ensemble Name InvAriant Graph RepresentAtion. NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving. Our experimental evaluation shows state-of-the-art performance on multiple datasets from different domains with improvements up to 10% compared to the best learning-based approaches. Furthermore, transfer learning experiments show that our approach significantly outperforms other learning-based approaches by up to 28%.

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AAAI Conference 2023 Conference Paper

Foundation Model for Material Science

Seiji Takeda
Akihiro Kishimoto
Lisa Hamada
Daiju Nakano
John R. Smith

Foundation models (FMs) are achieving remarkable successes to realize complex downstream tasks in domains including natural language and visions. In this paper, we propose building an FM for material science, which is trained with massive data across a wide variety of material domains and data modalities. Nowadays machine learning models play key roles in material discovery, particularly for property prediction and structure generation. However, those models have been independently developed to address only specific tasks without sharing more global knowledge. Development of an FM for material science will enable overarching modeling across material domains and data modalities by sharing their feature representations. We discuss fundamental challenges and required technologies to build an FM from the aspects of data preparation, model development, and downstream tasks.

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AAAI Conference 2022 Conference Paper

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization

Akihiro Kishimoto
Djallel Bouneffouf
Radu Marinescu
Parikshit Ram
Ambrish Rawat
Martin Wistuba
Paulito Palmes
Adi Botea

Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML. Despite recent progress, pipeline optimization remains a challenging problem, due to potentially many combinations to consider as well as slow training and validation. We present the BLDS algorithm for optimized algorithm selection in a fixed ML pipeline structure. BLDS performs multi-fidelity optimization for selecting ML algorithms trained with smaller computational overhead, while controlling its pipeline search based on multi-armed bandit and limited discrepancy search. Our experiments on classification benchmarks show that BLDS is superior to competing algorithms. We also combine BLDS with hyperparameter optimization, empirically showing the advantage of BLDS.

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SoCS Conference 2021 Conference Paper

Counting Vertex-Disjoint Shortest Paths in Graphs

Adi Botea
Massimiliano Mattetti
Akihiro Kishimoto
Radu Marinescu 0002
Elizabeth M. Daly

Finding a shortest path in a graph is at the core of many combinatorial search problems. A closely related problem refers to counting the number of shortest paths between two nodes. Such problems are solvable in polynomial time in the size of the graph. However, more realistic problem formulations could additionally specify constraints to satisfy. We study the problem of counting the shortest paths that are vertex disjoint and can satisfy additional constraints. Specifically, we look at the problems of counting vertex-disjoint shortest paths in edge-colored graphs, counting vertex-disjoint shortest paths with directional constraints, and counting vertex-disjoint shortest paths between multiple source-target pairs. We give a detailed theoretical analysis, and show formally that all of these three counting problems are NP-complete in general.