Publications





2025

  1. Reiya Asuke and Masahiro Yukawa, ``Adaptive Koopman Operator Learning via Iterative Projections: Time-series Data Prediction Using Extended Dynamic Mode Decomposition,'' IEEE Access, accepted for publication.

  2. Kyohei Suzuki and Masahiro Yukawa, ``External Division of Two Proximity Operators --- Part II: Generalization and Properties,'' IEEE Trans. Signal Processing, vol.73, pp.--, 2025, accepted for publication.

  3. Kyohei Suzuki and Masahiro Yukawa, ``External Division of Two Proximity Operators --- Part I: Debiased Feature Grouping,'' IEEE Trans. Signal Processing, vol.73, pp.--, 2025, accepted for publication.

  4. Masahiro Yukawa and Isao Yamada, ``Monotone Lipschitz-Gradient Denoiser: Explainability of Operator Regularization Approaches Free From Lipschitz Constant Control,'' IEEE Trans. Signal Processing, vol.73, pp.3378--3393, 2025.

  5. Masahiro Yukawa, ``Continuous Relaxation of Discontinuous Shrinkage Operator: Proximal Inclusion and Conversion,'' IEEE Open Journal of Signal Processing, vol.6, pp.753--767, 2025.

  6. Kyohei Suzuki and Masahiro Yukawa, ``A Discrete Measure for Debiased Feature Grouping: A Limit of Moreau-Enhanced OSCAR Regularizer and Its Proximity Operator,'' in Proceedings of EUSIPCO (European Signal Processing Conference), pp.2467--2471, Palermo: Italy, 2025.

  7. （招待講演） 湯川正裕, ``Plugging Monotone Lipschitz-Gradient Denoiser into Proximal Splitting Algorithms: A Lipschitz Control Free Approach and Explainability,’' RAMP数理最適化シンポジウム論文集, pp. 11–32, 2025 [pdf].

2024

  1. Maximilian Henri Vincent Tillmann and Masahiro Yukawa, ``Stable Outlier-Robust Signal Recovery Over Networks: A Convex Analytic Approach Using Minimax Concave Loss,'' IEEE Trans. Signal and Information Processing over Networks, vol.10, pp.690--705, 2024.

  2. Chengcheng Wang, Ye Wei, and Masahiro Yukawa, ``Dispersed-Sparsity-Aware LMS Algorithm for Scattering-Sparse System Identification,'' Signal Processing, Elsevier, vol.225, pp.1--16, Dec. 2024.

  3. Navneet Agrawal, Renato L.G. Cavalcante, Masahiro Yukawa, and Slawomir Stanczak, ``Distributed Convex Optimization ``Over-the-Air'' in Dynamic Environments,'' IEEE Trans. Signal and Information Processing over Networks, vol.10, pp.610--625, 2024.

  4. Daiki Sawada and Masahiro Yukawa, ``Robust Adaptive Filtering Based on Adaptive Projected Subgradient Method: Moreau Enhancement of Distance Function,'' APSIPA-ASC, Macao, 2024.

  5. Yoshifumi Shoji and Masahiro Yukawa, ``Robust Quantile Regression Under Unreliable Data,'' APSIPA-ASC, Macao, 2024.

  6. Kohei Yoshida and Masahiro Yukawa, ``Robust Method for Network Topology Identification under Structural Equation Model,'' in Proceedings of IEEE MLSP, pp.1--6, London: UK, September 2024.

  7. Ryotaro Kadowaki and Masahiro Yukawa, ``LiMES-SVM: A robust classification approach bridging soft-margin and hard-margin SVMs,'' submitted. in Proceedings of IEEE MLSP, pp.1--6, London: UK, September 2024.

  8. Naoto Kaneko and Masahiro Yukawa, Renato L. G. Cavalcante, and Lorenzo Miretti, ``Robust Estimation of Angular Power Spectrum in Massive MIMO Under Covariance Estimation Errors: Learning Centers and Scales of Gaussians,'' in Proceedings of IEEE SPAWC, pp.576--580, Lucca: Italy, September 2024.

  9. Kyohei Suzuki and Masahiro Yukawa, ``External Division of Two Proximity Operators: an Application to Signal Recovery with Structured Sparsity,'' in Proceedings of 49th IEEE ICASSP, pp.9471--9475, Seoul, South Korea, April 2024.

2023

  1. Daniyal Amir Awan, Renato L.G. Cavalcante, Masahiro Yukawa, and Slawomir Stanczak, ``Robust Online Multiuser Detection: A Hybrid Model-Data Driven Approach,'' IEEE Trans. Signal Processing, vol.71, pp.2103--2117, 2023.

  2. Masahiro Yukawa, Hiroyuki Kaneko, Kyohei Suzuki, Isao Yamada, ``Linearly-involved Moreau-Enhanced-over-Subspace Model: Debiased Sparse Modeling and Stable Outlier-Robust Regression'' IEEE Trans. Signal Processing, vol.71, pp.1232--1247, 2023.

  3. Kyohei Suzuki and Masahiro Yukawa, ``Sparse Stable Outlier-Robust Signal Recovery Under Gaussian Noise,'' IEEE Trans. Signal Processing, vol.71, pp.372--387, 2023.

  4. Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez, Antonio Ortega, ``Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields,'' IEICE Trans. Fundamentals, vol.E106-A, no.1, pp.23—34, January 2023.

  5. Keisuke Takazawa, Hirokazu Kameoka, and Masahiro Yukawa, ``Multiple Sound Source Tracking Based on Generative Modeling and Recursive Bayesian Filtering of Spatial Gradient Spectra,'' In Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Taipei: Taiwan, pp. 2008--2012, November 2023.

  6. Maximilian Henri Vincent Tillmann and Masahiro Yukawa, ``Distributed Stable Outlier-Robust Signal Recovery Using Minimax Concave Loss,'' in Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 6 pages, Rome: Italy, 2023.

  7. Takumi Ichinose, Masahiro Yukawa, and R.L.G. Cavalcante, ``Online Kernel-Based Quantile Regression Using Huberized Pinball Loss,'' in Proceedings of EUSIPCO (European Signal Processing Conference), pp.1803-1807, Helsinki, September 2023.

2022

  1. Kei Komuro, Masahiro Yukawa, and Renato L.G. Cavalcante, ``Distributed Sparse Optimization with Weakly Convex Regularizer: Consensus Promoting and Approximate Moreau Enhanced Penalties towards Global Optimality," IEEE Trans. Signal and Information Processing over Networks, vol.8, pp.514--527, 2022.

  2. Eiji Ninomiya, Masahiro Yukawa, Renato L. G. Cavalcante and Lorenzo Miretti, ``Estimation of Angular Power Spectrum Using Multikernel Adaptive Filtering,'' Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai: Thailand, pp. 1996--2000, November 2022. (doi: 10.23919/APSIPAASC55919.2022.9980067)

  3. Tatsuya Koyakumaru and Masahiro Yukawa, ``An Efficient Robust Graph Learning Approach Based on Minimax Concave Penalty and γ-Cross Entropy," in Proceedings of EUSIPCO (European Signal Processing Conference), pp.1776--1780, August--September 2022.

  4. Masahiro Yukawa, Kyohei Suzuki, and Isao Yamada, ``'Stable Robust Regression under Sparse Outlier and Gaussian Noise," in Proceedings of EUSIPCO (European Signal Processing Conference), pp.2236--2240, August--September 2022.

  5. Kei Komuro, Masahiro Yukawa, and Renato L.G. Cavalcante, ``Distributed Sparse Optimization Based on Minimax Concave and Consensus Promoting Penalties: Towards Global Optimality," in Proceedings of EUSIPCO (European Signal Processing Conference), pp.1841--1845, August--September 2022.

  6. Kyohei Suzuki and Masahiro Yukawa, ``On Grouping Effect Of Sparse Stable Outlier-Robust Regression," submitted. in Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 6 pages, 2022.

  7. Kyohei Suzuki and Masahiro Yukawa, ``Sparse Stable Outlier-Robust Regression with Minimax Concave Function," submitted. in Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 6 pages, August 2022.

  8. 湯川正裕, 多カーネル適応フィルタが拓く無線通信技術の未来： 部分線形フィルタによる多ユーザ通信用ロバストシンボル検出法, 信学技報, vol. 122, no. 155, SRW2022-15 （招待講演）, pp. 33--38, 招待講演, 2022.

  9. 湯川正裕, 第一回：凸解析的アプローチへのプロムナード, 日本音響学会誌, 連載講座：非線形適応信号処理への凸解析的アプローチ, 78巻 8号, pp. 457--464, 2022. [無料公開]

  10. 湯川正裕， 第二回：凸解析に基づく信号処理への招待, 日本音響学会誌, 連載講座：非線形適応信号処理への凸解析的アプローチ, 78巻 9号, pp.540--547, 2022. [無料公開]

  11. 湯川正裕，第三回：不動点近似型適応アルゴリズムと応用, 日本音響学会誌, 連載講座：非線形適応信号処理への凸解析的アプローチ, 78巻 10号, pp.606--614, 2022. [無料公開]

  12. 湯川正裕, 第四回：再生核に基づく非線形数理モデル, 日本音響学会誌, 連載講座：非線形適応信号処理への凸解析的アプローチ, 78巻 11号, pp.692--699, 2022. [無料公開]

  13. 湯川正裕, 第五回(最終): 非線形適応フィルタと応用：再生核と不動点近似型 アルゴリズムの出会い, 日本音響学会誌, 連載講座：非線形適応信号処理への凸解析的アプローチ, 78巻 12号, pp.730--739, 2022. [無料公開]

2021

  1. T. Kono, M. Yukawa, and T. Piotrowski, ``Relaxed Zero-Forcing Beamformer under Temporally-Correlated Interference,'' Signal Processing, Elsevier, vol.190, pp.1--14, Jan. 2021.

  2. Kwangjin Jeong and Masahiro Yukawa, ``Kernel weights for equalizing kernel-wise convergence rates of multikernel adaptive filtering," IEICE Trans. Fundamentals, vol.E104-A, no.6, pp.927—939, June 2021.

  3. Masaaki Takizawa and Masahiro Yukawa, ``Joint learning of model parameters and coefficients for online nonlinear estimation," IEEE Access, vol.9, pp.24026--24040, 2021. (Publication: January 2021)

  4. Kyohei Suzuki and Masahiro Yukawa, ``Robust recovery of jointly-sparse signals using minimax concave loss function," IEEE Trans. Signal Processing, vol.69, pp.669--681, 2021. (Article DOI: 10.1109/TSP.2020.3044445) (Publication: December 2020)

  5. Masa-aki Takizawa and Masahiro Yukawa, ``A Hilbertian Projection Approach with Dictionary Dividing Strategy: Accelerating Nonlinear Estimation Algorithm with Multiscale Gaussians,'' in Proceedings of APSIPA Annual Summit and Conference, Special Session: Online and Distributed Kernel Learning Algorithms, Dec. 2021.

  6. Kei Komuro, Masahiro Yukawa, and Renato L.G. Cavalcante, ``Distributed sparse optimization with minimax concave regularization," in Proceedings of IEEE Statistical Signal Processing Workshop, Special Session: Signal Modelling, Adaptive Learning and Applications, pp.1--5, July 2021.

  7. Konstantinos Slavakis, Masahiro Yukawa, ``Outlier-robust kernel hierarchical-optimization rls on a budget with affine constraints," in Proceedings of 46th IEEE ICASSP, pp.5315--5319 , 2021.

  8. Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez, Antonio Ortega, ``A Graph Learning Algorithm Based on Gaussian Markov Random Fields and Minimax Concave Penalty," in Proceedings of 46th IEEE ICASSP, pp.5390--5394, 2021.

  9. Kyohei Suzuki and Masahiro Yukawa, ``Robust jointly-sparse signal recovery based on minimax concave loss function," in Proceedings of EUSIPCO (European Signal Processing Conference), pp.2070--2074, January 2021.

all publications (personal website of masahiro yukawa)