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ORCIDShuhei Watanabe
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2020 – today
- 2024
[j5]Shusuke Shigenaka
, Shunki Takami, Yuki Tanigaki, Shuhei Watanabe, Masaki Onishi:
MAS-Bench: a benchmarking for parameter calibration of multi-agent crowd simulation. J. Comput. Soc. Sci. 7(2): 2121-2145 (2024)- [j4]Shuhei Watanabe, Takahiko Horiuchi:
Layered Modeling of Affective, Perception, and Visual Properties: Optimizing Structure With Genetic Algorithm. IEEE Trans. Hum. Mach. Syst. 54(5): 609-618 (2024) - [c17]Gen Sato, Shun Shiramatsu, Mizuki Hoshino, Shuhei Watanabe, Yu Haibo, Takeshi Mizumoto:
LLM-Based Structuring of Oral Discussion in Workshop to Support Collaboration Among Local Government and Simulated Citizens. CollabTech 2024: 3-16 - [i8]Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter:
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks. CoRR abs/2403.01888 (2024) - 2023
- [c16]Shuhei Watanabe, Frank Hutter:
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization. IJCAI 2023: 4371-4379 - [c15]Shuhei Watanabe, Noor H. Awad, Masaki Onishi, Frank Hutter:
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator. IJCAI 2023: 4380-4388 - [c14]Shuhei Watanabe, Archit Bansal, Frank Hutter:
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. IJCAI 2023: 4389-4396 - [i7]Shuhei Watanabe, Archit Bansal, Frank Hutter:
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. CoRR abs/2304.10255 (2023) - [i6]Shuhei Watanabe:
Tree-structured Parzen estimator: Understanding its algorithm components and their roles for better empirical performance. CoRR abs/2304.11127 (2023) - [i5]Shuhei Watanabe:
Python Tool for Visualizing Variability of Pareto Fronts over Multiple Runs. CoRR abs/2305.08852 (2023) - [i4]Shuhei Watanabe:
Python Wrapper for Simulating Multi-Fidelity Optimization on HPO Benchmarks without Any Wait. CoRR abs/2305.17595 (2023) - 2022
- [j3]Yoshihiko Ozaki, Yuki Tanigaki, Shuhei Watanabe, Masahiro Nomura, Masaki Onishi:
Multiobjective Tree-Structured Parzen Estimator. J. Artif. Intell. Res. 73: 1209-1250 (2022) - [c13]Shuhei Watanabe, Takahiko Horiuchi:
Layered Perceptual Modeling Using Structural Equation Modeling: Exploring Structure with Genetic Algorithm. SMC 2022: 574-579 - [i3]Shuhei Watanabe, Frank Hutter:
c-TPE: Generalizing Tree-structured Parzen Estimator with Inequality Constraints for Continuous and Categorical Hyperparameter Optimization. CoRR abs/2211.14411 (2022) - [i2]Shuhei Watanabe, Noor H. Awad, Masaki Onishi, Frank Hutter:
Multi-objective Tree-structured Parzen Estimator Meets Meta-learning. CoRR abs/2212.06751 (2022) - 2021
- [c12]Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto, Yoshihiko Ozaki, Masaki Onishi:
Warm Starting CMA-ES for Hyperparameter Optimization. AAAI 2021: 9188-9196 - [c11]Shusuke Shigenaka, Shunki Takami, Shuhei Watanabe, Yuki Tanigaki, Yoshihiko Ozaki, Masaki Onishi:
MAS-Bench: Parameter Optimization Benchmark for Multi-agent Crowd Simulation. AAMAS 2021: 1652-1654 - [c10]Shuhei Watanabe, Takahiko Horiuchi:
Image-based Perceptual Editing: Leather "Authenticity" as a Case Study. Material Appearance 2021: 1-10 - [c9]Shuhei Watanabe, Shoji Tominaga, Takahiko Horiuchi:
The Difference in Impression between Genuine and Artificial Leather: Quantifying the Feeling of Authenticity. HVEI 2021 - 2020
- [j2]Shuhei Watanabe, Shoji Tominaga, Takahiko Horiuchi:
The Difference in Impression between Genuine and Artificial Leather: Quantifying the Feeling of Authenticity. J. Percept. Imaging 3(2): 20501-1 (2020) - [c8]Shuhei Watanabe, Takahiko Horiuchi:
Hierarchical Model of "Feeling of Luxury: " Genuine and Artificial Leather Case Study. AHFE (2) 2020: 763-769 - [c7]Shuhei Watanabe:
One-shot Multi-angle Measurement Device for Evaluating the Sparkle Impression. Material Appearance 2020: 1-8 - [c6]Yoshihiko Ozaki, Yuki Tanigaki, Shuhei Watanabe, Masaki Onishi:
Multiobjective tree-structured parzen estimator for computationally expensive optimization problems. GECCO 2020: 533-541 - [c5]Shintaro Takenaga, Shuhei Watanabe, Masahiro Nomura, Yoshihiko Ozaki, Masaki Onishi, Hitoshi Habe:
Evaluating Initialization of Nelder-Mead Method for Hyperparameter Optimization in Deep Learning. ICPR 2020: 3372-3379 - [i1]Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto, Yoshihiko Ozaki, Masaki Onishi:
Warm Starting CMA-ES for Hyperparameter Optimization. CoRR abs/2012.06932 (2020)
2010 – 2019
- 2019
- [c4]Shuhei Watanabe, Yoshihiko Ozaki, Yoshiaki Bando, Masaki Onishi:
Speeding up of the Nelder-Mead Method by Data-Driven Speculative Execution. ACPR (1) 2019: 438-452 - [c3]Shuhei Watanabe, Takuroh Sone:
Evaluation of Sparkle Impression Considering Observation Distance. Material Appearance 2019: 1-5 - 2017
- [c2]Takuroh Sone, Shuhei Watanabe:
Measurement and Evaluation Method of Orange Peel. Material Appearance 2017: 62-65
2000 – 2009
- 2002
- [j1]Atsushi Koike, Shin-Ichi Nakano, Takao Nishizeki, Takeshi Tokuyama, Shuhei Watanabe:
Labeling Points with Rectangles of Various Shapes. Int. J. Comput. Geom. Appl. 12(6): 511-528 (2002) - 2000
- [c1]Shin-Ichi Nakano, Takao Nishizeki, Takeshi Tokuyama, Shuhei Watanabe:
Labeling Points with Rectangles of Various Shapes. GD 2000: 91-102
Coauthor Index
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last updated on 2024-10-23 20:36 CEST by the dblp team
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