プロフィール
略歴
SB Intuitionsにシニアリサーチサイエンティストとして，京都大学に特定助教として所属しています． 2025年まで理化学研究所にてポスドク研究員として勤務していました． 2022年に東京大学にて情報理工学の博士号を取得しました．
最近の情報
2026
- 論文 “RAPTOR: Accurate and Scalable Rational Polar Decomposition for the Muon Algorithm” がECML PKDD 2026に採択されました．
- 筑波大・福地さん，京大・松井さんとの共著論文 “Provable Data Scaling Law for Meta Learning via Complexity Minimization” がICML 2026のHigh-dimensional Learning Dynamics (HiLD)ワークショップに採択されました．
- 筑波大・福地さん，京大・松井さんとの共著論文 “Provable Target Sample Complexity Improvements as Pre‑Trained Models Scale” がAISTATS 2026に採択されました．
過去の情報
2025
- Artois UniversityおよびInria Centre at the University of Bordeauxを訪問します🇫🇷．
- 翻訳プロジェクトに参加したKevin Murphy著 “Probabilistic Machine Learning: An Introduction” が「確率的機械学習」として朝倉書店から出版されます（上, 下）．
- 京都大学に特定助教として着任します．
- 主著論文 “An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration” がTMLRに採択されました．
- ポーランドのニコラウス・コペルニクス大学を訪問し、NOWで招待講演を行います🇵🇱．
- 研究課題がJST BOOST 次世代 AI 人材育成プログラムに採択されました．
- 研究課題がJSPS 若手研究に採択されました．
- 理研AIPを退職し，SB Intuitionsにシニアリサーチサイエンティストとして入社します．
- 秋田大学において講義を行いました．
2024
- 医療健康データ科学Webセミナーの機械学習コースにおいて「深層学習の基礎」について講演します．
- メルボルン大学を訪問します🇦🇺．
- IBIS 2024に参加します．
- に参加します．
- ERCIM/JST Joint Workshop 2024に参加します🇭🇺．
- University of Trentoにおいて招待講演 “Gradient-based hyperparameter optimization for deep learning” を行います．
- IIT，University of Trento，およびFondazione Bruno Kesslerを訪問します🇮🇹．
- ECCVに参加します🇮🇹．
- CENTAI🇮🇹を訪問します．
- ICMLに参加します🇦🇹．
- Aix-Marseiile大学🇫🇷を訪問し，Workshop on mathematical foundations of machine learningにおいて発表します．
- A*STAR-CFAR🇸🇬に滞在し，A*STAR-CFAR_RIKEN-AIP Workshop に参加します．
- Zapping seminar において招待講演 “Automatic Domain Adaptation by Transformers in In-Context Learning” を行います．
- RIKEN-IIT Joint Workshop in ML/AIに参加します．
- 京都大学・包さんとの共著論文 “Self-attention Networks Localize When QK-eigenspectrum Concentrates” がICMLに採択されました．
- ECCV 2024において，ワークショップ “The Dark Side of Generative AIs and Beyond” を開催します．
- 研究課題が東京大学情報基盤センター 若手・女性利用者推薦に採択されました．
- 研究課題がJST ACT-X数理情報のフロンティア領域の加速フェーズに採択されました．
- Workshop on Functional Inference and Machine Intelligenceに参加します．
- DL 2024に参加します．
- Machine Learning Summer School Okinawaにプログラム委員として参加します．
- 第53回IBISML研究会において招待講演「勾配法を用いたハイパーパラメータ最適化」を行います．
- 統数研共同研究集会 兼 第4回TREFOIL研究会で招待講演「深層学習とデータ拡張」を行います．
2023
- 国立がん研究センター・小林さんとの共著論文 “Sketch-based Semantic Retrieval of Medical Images” がMedical Image Analysis誌に採択されました．プレスリリース．
- スイスのジュネーブで開催されるQTML 2023において"Non-commutative $C^\ast$-algebra Net"を発表します．
- モントリオール大/MILA・長沼さんとの共著論文 “An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration” がICCV 2023 Workshop on Uncertainty Quantification for Computer Visionに採択されました．
- 9月末にポーランドのニコラウス･コペルニクス大学を訪問します．
- 主著論文 “Will Large-scale Generative Models Corrupt Future Datasets?” がICCV 2023に採択されました．京大・包さん，理研・荒井さんとの共著です．
- 国立がん研究センター・小林さんとの共著論文 “Towards AI-driven radiology education: A self-supervised segmentation-based framework for high-precision medical image editing” がMICCAI2023にオーラルとして採択されました．
- 6月にモントリオールのMILAを訪問します．バンクーバーで開催されるCVPRに参加します．
- 東京大学素粒子物理国際研究センターで招待講演を行います．
- 5月にジェノバのIITを訪問します．
- AISTATSに参加します．
- 4月にVietnam Institute for Advanced Study in Mathematicsを訪問します．
- 3月にEPFL CIS・Fraunhofer IISを訪問します．
- 第4回理研AIP数学系合同セミナーに参加しました．
- 主著論文 “Nyström Method for Accurate and Scalable Implicit Differentiation” がAISTATS 2023に採択されました．京大・山田先生との共著です．
2022
- 理化学研究所の特別研究員となりました．
- 東京大学大学院情報理工学系研究科を修了し、博士（情報理工学）を授与されました．
- 7月8日からIIT(イタリア)を訪問します．
- 主著論文 “DJMix: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks " がIJCNN 2022に採択されました．
- 3月10日にザッピングセミナーにおいて発表を行います．
2021
- 12月2日に愛媛大学DS研究セミナーにおいて 深層学習とデータ拡張 を発表します．
- 10月1日からIIT（イタリア）を訪問しています.
- 主著論文 “Meta Approach to Data Augmentation Optimization"がWACV2022に採択されました．
- 研究提案がJSTのACT-Xに採択されました．
- JSPSの若手研究者海外挑戦プログラムにより10月からイタリアのIITにて訪問研究を行います．
- 画像の認識・理解シンポジウム（MIRU）で「iMADAO: 画像事例に応じたデータ拡張戦略の設計手法」を発表しました．
- NeurIPS meetupsの募集を開始しました．
- 東京大学RIISEの取材記事が公開されました
- 6月10日にSSIIのオーガナイズ土セッション「続・限られたデータからの深層学習」で「深層学習におけるデータ拡張の原理と最新動向」を発表します
- 5月12日に理研AIPのセミナーにおいてデータ拡張についての発表を行います（発表は英語です）．
- 主著論文 “Graph Energy-based Model for Molecular Graph Generation”がEBM Workshopにcontributed talkとして採択されました
- NeurIPS 2021のMeetup Chairとなりました
研究概要
研究の興味
- 勾配法によるハイパーパラメータ最適化
- 効率的でスケーラブルな深層学習
- 機械学習の他分野への応用（医用画像・古生物学・化学・物理学など）
記事
論文など
- 2026
- Sci. Data 2026MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning in the NISQ Era
Leonardo Placidi, Ryuichiro Hataya, Toshio Mori, Koki Aoyama, Hayata Morisaki, Kosuke Mitarai, Keisuke Fujii
Scientific Data, 2026PaperBibTeX
@article{placidi2026mnisq, author = {Placidi, Leonardo and Hataya, Ryuichiro and Mori, Toshio and Aoyama, Koki and Morisaki, Hayata and Mitarai, Kosuke and Fujii, Keisuke}, title = {{MNISQ}: A Large-Scale Quantum Circuit Dataset for Machine Learning in the {NISQ} Era}, journal = {Scientific Data}, year = {2026}, month = may, url = {https://www.nature.com/articles/s41597-026-07493-9}, } - arXiv 2026Which Geometry on Which Layer? Measured Sign-Spectral Assignment in Mixed-Optimizer Training
Hiroki Naganuma, Laura Gomezjurado, Mahdi Ghaznavi, Ganesh Talluri, Atsushi Nitanda, Seng Pei Liew, Ryuichiro Hataya, Ioannis Mitliagkas
arXiv, 2026PaperarXivBibTeX
@misc{naganuma2026geometry, title = {Which Geometry on Which Layer? Measured Sign-Spectral Assignment in Mixed-Optimizer Training}, author = {Hiroki Naganuma and Laura Gomezjurado and Mahdi Ghaznavi and Ganesh Talluri and Atsushi Nitanda and Seng Pei Liew and Ryuichiro Hataya and Ioannis Mitliagkas}, year = {2026}, eprint = {2610.17285}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, url = {https://arxiv.org/abs/2610.17285}, } - ECML PKDD 2026RAPTOR: Accurate and Scalable Rational Polar Decomposition for the Muon Algorithm
Ryuichiro Hataya
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2026PaperBibTeX
@inproceedings{hataya2026raptor, author = {Hataya, Ryuichiro}, title = {{RAPTOR}: Accurate and Scalable Rational Polar Decomposition for the Muon Algorithm}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases}, year = {2026}, url = {https://link.springer.com/chapter/10.1007/978-3-032-37670-1_3}, } - AISTATS 2026Provable Target Sample Complexity Improvements as Pre-Trained Models Scale
Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
International Conference on Artificial Intelligence and Statistics, 2026arXivBibTeX
@inproceedings{fukuchi2026provable, author = {Fukuchi, Kazuto and Hataya, Ryuichiro and Matsui, Kota}, title = {Provable Target Sample Complexity Improvements as Pre-Trained Models Scale}, booktitle = {International Conference on Artificial Intelligence and Statistics}, year = {2026}, } - ICML-W 2026Provable Data Scaling Law for Meta Learning via Complexity Minimization
Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
ICML Workshop on High-dimensional Learning Dynamics, 2026 · Peer-reviewed short paperBibTeX
@inproceedings{fukuchi2026scaling, author = {Fukuchi, Kazuto and Hataya, Ryuichiro and Matsui, Kota}, title = {Provable Data Scaling Law for Meta Learning via Complexity Minimization}, booktitle = {ICML Workshop on High-dimensional Learning Dynamics}, year = {2026}, } - 2025
- TMLR 2025An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma*, Ryuichiro Hataya*, Kotaro Yoshida, Ioannis Mitliagkas
Transactions on Machine Learning Research, 2025OpenReviewBibTeX
@article{naganuma2025empirical, author = {Naganuma, Hiroki and Hataya, Ryuichiro and Yoshida, Kotaro and Mitliagkas, Ioannis}, title = {An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration}, journal = {Transactions on Machine Learning Research}, year = {2025}, month = apr, } - 2024
- MedIA 2024Sketch-based Semantic Retrieval of Medical Images
Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Takaaki Mizuno, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Yasuyuki Takamizawa, Yukihiro Yoshida, Satoshi Nakamura, Nobuji Kouno, Amina Bolatkan, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
Medical Image Analysis, 2024PaperBibTeX
@article{kobayashi2024sketch, author = {Kobayashi, Kazuma and Gu, Lin and Hataya, Ryuichiro and Mizuno, Takaaki and Miyake, Mototaka and Watanabe, Hirokazu and Takahashi, Masamichi and Takamizawa, Yasuyuki and Yoshida, Yukihiro and Nakamura, Satoshi and Kouno, Nobuji and Bolatkan, Amina and Kurose, Yusuke and Harada, Tatsuya and Hamamoto, Ryuji}, title = {Sketch-based Semantic Retrieval of Medical Images}, journal = {Medical Image Analysis}, volume = {92}, year = {2024}, month = feb, url = {https://www.sciencedirect.com/science/article/pii/S1361841523003201}, } - ICML 2024Self-attention Networks Localize When QK-eigenspectrum Concentrates
Han Bao, Ryuichiro Hataya, Ryo Karakida
International Conference on Machine Learning, 2024PaperBibTeX
@inproceedings{bao2024selfattention, author = {Bao, Han and Hataya, Ryuichiro and Karakida, Ryo}, title = {Self-attention Networks Localize When {QK}-eigenspectrum Concentrates}, booktitle = {International Conference on Machine Learning}, year = {2024}, url = {https://proceedings.mlr.press/v235/bao24b.html}, } - arXiv 2024Quantum Circuit $C^\ast$-algebra Net
Yuka Hashimoto*, Ryuichiro Hataya*
arXiv, 2024arXivBibTeX
@misc{hashimoto2024quantum, author = {Hashimoto, Yuka and Hataya, Ryuichiro}, title = {Quantum Circuit $C^\ast$-algebra Net}, year = {2024}, eprint = {2404.06218}, archiveprefix = {arXiv}, } - arXiv 2024Glocal Hypergradient Estimation with Koopman Operator
Ryuichiro Hataya, Yoshinobu Kawahara
arXiv, 2024arXivBibTeX
@misc{hataya2024glocal, author = {Hataya, Ryuichiro and Kawahara, Yoshinobu}, title = {Glocal Hypergradient Estimation with {Koopman} Operator}, year = {2024}, eprint = {2402.02741}, archiveprefix = {arXiv}, } - ICML-W 2024Automatic Domain Adaptation by Transformers in In-Context Learning
Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi
ICML Workshop on In-Context Learning, 2024 · Peer-reviewed short paperPaperBibTeX
@inproceedings{hataya2024icl, author = {Hataya, Ryuichiro and Matsui, Kota and Imaizumi, Masaaki}, title = {Automatic Domain Adaptation by Transformers in In-Context Learning}, booktitle = {ICML Workshop on In-Context Learning}, year = {2024}, url = {https://arxiv.org/abs/2405.16819}, } - ICML-W 2024Transformers as Stochastic Optimizers
Ryuichiro Hataya, Masaaki Imaizumi
ICML Workshop on In-Context Learning, 2024 · Peer-reviewed short paperBibTeX
@inproceedings{hataya2024stochastic, author = {Hataya, Ryuichiro and Imaizumi, Masaaki}, title = {Transformers as Stochastic Optimizers}, booktitle = {ICML Workshop on In-Context Learning}, year = {2024}, } - 2023
- ICCV 2023Will Large-scale Generative Models Corrupt Future Datasets?
Ryuichiro Hataya, Han Bao, Hiromi Arai
International Conference on Computer Vision, 2023PaperBibTeX
@inproceedings{hataya2023corrupt, author = {Hataya, Ryuichiro and Bao, Han and Arai, Hiromi}, title = {Will Large-scale Generative Models Corrupt Future Datasets?}, booktitle = {International Conference on Computer Vision}, year = {2023}, url = {https://openaccess.thecvf.com/content/ICCV2023/html/Hataya_Will_Large-scale_Generative_Models_Corrupt_Future_Datasets_ICCV_2023_paper.html}, } - MICCAI 2023Towards AI-driven Radiology Education: A Self-supervised Segmentation-based Framework for High-precision Medical Image Editing
Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Mototaka Miyake, Yasuyuki Takamizawa, Sono Ito, Hirokazu Watanabe, Yukihiro Yoshida, Hiroki Yoshimura, Tatsuya Harada, Ryuji Hamamoto
Medical Image Computing and Computer-Assisted Intervention, 2023 · OralBibTeX
@inproceedings{kobayashi2023radiology, author = {Kobayashi, Kazuma and Gu, Lin and Hataya, Ryuichiro and Miyake, Mototaka and Takamizawa, Yasuyuki and Ito, Sono and Watanabe, Hirokazu and Yoshida, Yukihiro and Yoshimura, Hiroki and Harada, Tatsuya and Hamamoto, Ryuji}, title = {Towards {AI}-driven Radiology Education: A Self-supervised Segmentation-based Framework for High-precision Medical Image Editing}, booktitle = {Medical Image Computing and Computer-Assisted Intervention}, year = {2023}, } - AISTATS 2023Nyström Method for Accurate and Scalable Implicit Differentiation
Ryuichiro Hataya, Makoto Yamada
International Conference on Artificial Intelligence and Statistics, 2023arXivBibTeX
@inproceedings{hataya2023nystrom, author = {Hataya, Ryuichiro and Yamada, Makoto}, title = {Nystr{\"o}m Method for Accurate and Scalable Implicit Differentiation}, booktitle = {International Conference on Artificial Intelligence and Statistics}, year = {2023}, } - arXiv 2023Noncommutative $C^\ast$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^\ast$-algebra
Ryuichiro Hataya*, Yuka Hashimoto*
arXiv, 2023arXivBibTeX
@misc{hataya2023noncommutative, author = {Hataya, Ryuichiro and Hashimoto, Yuka}, title = {Noncommutative $C^\ast$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^\ast$-algebra}, year = {2023}, eprint = {2302.01191}, archiveprefix = {arXiv}, } - QTML 2023Noncommutative $C^\ast$-algebra Nets
Ryuichiro Hataya, Yuka Hashimoto
International Conference on Quantum Techniques in Machine Learning, 2023 · Peer-reviewed extended abstractBibTeX
@inproceedings{hataya2023qtml, author = {Hataya, Ryuichiro and Hashimoto, Yuka}, title = {Noncommutative $C^\ast$-algebra Nets}, booktitle = {International Conference on Quantum Techniques in Machine Learning}, year = {2023}, } - ICCV-W 2023An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma, Ryuichiro Hataya
ICCV Workshop on Uncertainty Quantification for Computer Vision, 2023 · Peer-reviewed extended abstractarXivBibTeX
@inproceedings{naganuma2023empirical, author = {Naganuma, Hiroki and Hataya, Ryuichiro}, title = {An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration}, booktitle = {ICCV Workshop on Uncertainty Quantification for Computer Vision}, year = {2023}, } - 2022
- IJCNN 2022DJMix: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks
Ryuichiro Hataya, Hideki Nakayama
International Joint Conference on Neural Networks, 2022BibTeX
@inproceedings{hataya2022djmix, author = {Hataya, Ryuichiro and Nakayama, Hideki}, title = {{DJMix}: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks}, booktitle = {International Joint Conference on Neural Networks}, year = {2022}, } - WACV 2022Meta Approach to Data Augmentation Optimization
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama
Winter Conference on Applications of Computer Vision, 2022PDFBibTeX
@inproceedings{hataya2022meta, author = {Hataya, Ryuichiro and Zdenek, Jan and Yoshizoe, Kazuki and Nakayama, Hideki}, title = {Meta Approach to Data Augmentation Optimization}, booktitle = {Winter Conference on Applications of Computer Vision}, year = {2022}, } - 2021
- MedIA 2021Decomposing Normal and Abnormal Features of Medical Images for Content-Based Image Retrieval of Glioma Imaging
Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Mototaka Miyake, Masamichi Takahashi, Akiko Nakagawa, Tatsuya Harada, Ryuji Hamamoto
Medical Image Analysis, 2021PaperBibTeX
@article{kobayashi2021decomposing, author = {Kobayashi, Kazuma and Hataya, Ryuichiro and Kurose, Yusuke and Miyake, Mototaka and Takahashi, Masamichi and Nakagawa, Akiko and Harada, Tatsuya and Hamamoto, Ryuji}, title = {Decomposing Normal and Abnormal Features of Medical Images for Content-Based Image Retrieval of Glioma Imaging}, journal = {Medical Image Analysis}, volume = {74}, year = {2021}, month = dec, url = {https://www.sciencedirect.com/science/article/pii/S1361841521002723}, } - ICASSP 2021Visualizing Association in Exemplar-based Classification
Taiga Kashima, Ryuichiro Hataya, Hideki Nakayama
International Conference on Acoustics, Speech, and Signal Processing, 2021BibTeX
@inproceedings{kashima2021visualizing, author = {Kashima, Taiga and Hataya, Ryuichiro and Nakayama, Hideki}, title = {Visualizing Association in Exemplar-based Classification}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, year = {2021}, } - arXiv 2021Graph Energy-based Model for Substructure Preserving Molecular Design
Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe
arXiv, 2021arXivBibTeX
@misc{hataya2021graph, author = {Hataya, Ryuichiro and Nakayama, Hideki and Yoshizoe, Kazuki}, title = {Graph Energy-based Model for Substructure Preserving Molecular Design}, year = {2021}, eprint = {2102.04600}, archiveprefix = {arXiv}, } - ICLR-W 2021Graph Energy-based Model for Molecular Graph Generation
Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe
EBM Workshop at ICLR, 2021 · Contributed talkOpenReviewBibTeX
@inproceedings{hataya2021graphebm, author = {Hataya, Ryuichiro and Nakayama, Hideki and Yoshizoe, Kazuki}, title = {Graph Energy-based Model for Molecular Graph Generation}, booktitle = {EBM Workshop at ICLR}, year = {2021}, } - 2020
- ECCV 2020Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama
European Conference on Computer Vision, 2020PaperBibTeX
@inproceedings{hataya2020faster, author = {Hataya, Ryuichiro and Zdenek, Jan and Yoshizoe, Kazuki and Nakayama, Hideki}, title = {Faster {AutoAugment}: Learning Augmentation Strategies using Backpropagation}, booktitle = {European Conference on Computer Vision}, year = {2020}, url = {http://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4830_ECCV_2020_paper.php}, } - ML4H 2020Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval
Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
Machine Learning for Health Workshop at NeurIPS, 2020 · Peer-reviewed extended abstractBibTeX
@inproceedings{kobayashi2020decomposing, author = {Kobayashi, Kazuma and Hataya, Ryuichiro and Kurose, Yusuke and Harada, Tatsuya and Hamamoto, Ryuji}, title = {Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval}, booktitle = {Machine Learning for Health Workshop at NeurIPS}, year = {2020}, } - GSA 2020Learning to Identify Large Fossils using Deep Convolutional Neural Networks
Ryuichiro Hataya, Kumiko Matsui, Tomoki Karasawa
Geological Society of America Abstracts with Programs, 2020BibTeX
@inproceedings{hataya2020fossils, author = {Hataya, Ryuichiro and Matsui, Kumiko and Karasawa, Tomoki}, title = {Learning to Identify Large Fossils using Deep Convolutional Neural Networks}, booktitle = {Geological Society of America Abstracts with Programs}, volume = {52}, number = {6}, year = {2020}, } - 2019
- ICIP 2019LOL: Learning To Optimize Loss Switching Under Label Noise
Ryuichiro Hataya, Hideki Nakayama
International Conference on Image Processing, 2019BibTeX
@inproceedings{hataya2019lol, author = {Hataya, Ryuichiro and Nakayama, Hideki}, title = {{LOL}: Learning To Optimize Loss Switching Under Label Noise}, booktitle = {International Conference on Image Processing}, year = {2019}, } - ICLR-W 2019Unifying Semi-supervised and Robust Learning by Mixup
Ryuichiro Hataya, Hideki Nakayama
Workshop on Learning from Limited Labeled Data at ICLR, 2019 · SpotlightBibTeX
@inproceedings{hataya2019unifying, author = {Hataya, Ryuichiro and Nakayama, Hideki}, title = {Unifying Semi-supervised and Robust Learning by Mixup}, booktitle = {Workshop on Learning from Limited Labeled Data at ICLR}, year = {2019}, }
* 同等の貢献
研究活動
招待講演
- “Developing Japanese Vision Language Models,” NOW, ニコラスコペルニクス大学, 2025年1月
過去の情報
- 「深層学習の基礎」，医療健康データ科学Webセミナー，2024年12月 スライド
- “Gradient-based Hyperparameter Optimization for Deep Learning”, トレント大学, 2024年10月
- “Will Large-scale Generative Models Corrupt Future Datasets?"，FIT2024，2024年9月
- “Automatic Domain Adaptation by Transformers in In-Context Learning,” Workshop on Mathematical foundations of machine learning, 2024年7月
- “Automatic Domain Adaptation by Transformers in In-Context Learning,” A*STAR-CFAR_RIKEN-AIP Workshop, 2024年6月 スライド
- “Automatic Domain Adaptation by Transformers in In-Context Learning,” ザッピングセミナー, 2024年6月 スライド
- 「勾配法によるハイパーパラメータ最適化」，第53回IBISML研究会，2024年3月 スライド．
- 「深層学習とデータ拡張」，統数研共同研究集会 兼 第4回TREFOIL研究会，2024年2月．
- “行列式点過程の機械学習への応用,” ICEPPセミナー, 2023年6月．
- “Towards accurate and scalable gradient-based hyperparameter optimization,” Istituto Italiano di Tecnologia, 2023年5月．
- “Noncommutative $C^\star$-algebra Nets,” Japan-Vietnam AI Forum, 2023年4月.
- 「ベイズモデル選択による弱教師あり深層学習アルゴリズムのハイパーパラメータ最適化」, ザッピングセミナー, 2022.
- 「深層学習とデータ拡張」, 愛媛大学DS研究セミナー，2021.
- 「深層学習におけるデータ拡張の原理と最新動向」, Symposium on Sensing via Image Information, 2021.
- 「深層学習を支えるデータ拡張」, StatsML Symposium, 2020.
- 「勾配降下法によるハイパーパラメータ最適化とデータ拡張戦略最適化への応用」, ザッピングセミナー , 2020.
講演
- 「生成モデルによるデータセット汚染」, 人工知能学会, 静岡, 2024年5月.
- 「生成モデルによるデータセット汚染」, 数理情報研究集会, 秋田, 2023年10月.
- “Stable Gradient-based Hyperparameter Optimization,” RIKEN AIP & NCU Workshops 2023, September 2023.
研究費
- BOOST次世代 AI 人材育成プログラム，JST，5,000万円，2025-2029．
過去の情報
* 若手研究，JSPS，320万円，2025-2026． * ACT-X加速フェーズ，JST，500万円，2024． * 研究活動スタート支援，JSPS，220万円，2023-2024. * ACT-X, 450万円, JST，2021-2024. * JSPS若手研究者海外挑戦プログラム, 140万円, 2021. * Microsoft Research Asia Collaborative Research Program (D-CORE 2021) by MSRA, 100万円, 2021. * 東大RIISE価値交換工学萌芽RA, 200万円, 2020-2022.受賞
- Microsoft Research Asia D-Core Award, 2020.
- Best Student Paper Award, The 23rd Meeting on Image Recognition and Understanding, 2020.
そのほか
- Reviewer for NeurIPS, ICCV, ICLR, CVPR, ICML, ECCV 2019~.
過去の情報
- Organizer of ECCV 2024 Workshop The Dark Side of Generative AIs and Beyond.
- Organizer of YAML 2024.
- Program Committee of MLSS Okinawa 2024.
- Program Committee of IBIS 2023.
- Meetup Chair of NeurIPS, 2021.
- Organizer of NeurIPS meetup Japan & Women in ML, 2020.
- Volunteer for ICML, and ICLR, 2020.
連絡先
research@${last_name}.tokyoからご連絡ください．