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ORCIDShinichi Nakajima
Person information
- affiliation: Berlin Big Data Center, Germany
- affiliation: TU Berlin, Machine Learning Group, Germany
- affiliation: Nikon Corporation, Tokyo, Japan
- affiliation: Tokyo Institute of Technology, Japan
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Journal Articles
- 2023
[j21]Vignesh Srinivasan
, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Langevin Cooling for Unsupervised Domain Translation. IEEE Trans. Neural Networks Learn. Syst. 34(10): 7675-7688 (2023)- 2022
- [j20]Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel:
Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. Mach. Learn. Sci. Technol. 3(4): 45006 (2022) - [j19]Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon:
Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7581-7596 (2022) - [j18]Susanna Schwarzmann, Clarissa Cassales Marquezan, Riccardo Trivisonno, Shinichi Nakajima, Vincent Barriac, Thomas Zinner:
ML-Based QoE Estimation in 5G Networks Using Different Regression Techniques. IEEE Trans. Netw. Serv. Manag. 19(3): 3516-3532 (2022) - 2021
- [j17]Vignesh Srinivasan, Csaba Rohrer, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Robustifying models against adversarial attacks by Langevin dynamics. Neural Networks 137: 1-17 (2021) - 2020
- [j16]Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Optimizing for Measure of Performance in Max-Margin Parsing. IEEE Trans. Neural Networks Learn. Syst. 31(7): 2680-2684 (2020) - 2018
- [j15]Stephan Kaltenstadler, Shinichi Nakajima, Klaus-Robert Müller, Wojciech Samek:
Wasserstein Stationary Subspace Analysis. IEEE J. Sel. Top. Signal Process. 12(6): 1213-1223 (2018) - [j14]Nico Görnitz, Luiz Alberto Lima, Luiz Eduardo Varella, Klaus-Robert Müller, Shinichi Nakajima:
Transductive Regression for Data With Latent Dependence Structure. IEEE Trans. Neural Networks Learn. Syst. 29(7): 2743-2756 (2018) - [j13]Nico Görnitz, Luiz Alberto Lima, Klaus-Robert Müller, Marius Kloft, Shinichi Nakajima:
Support Vector Data Descriptions and k-Means Clustering: One Class? IEEE Trans. Neural Networks Learn. Syst. 29(9): 3994-4006 (2018) - 2017
- [j12]Luiz Alberto Lima, Nico Görnitz, Luiz Eduardo Varella, Marley M. B. R. Vellasco, Klaus-Robert Müller, Shinichi Nakajima:
Porosity estimation by semi-supervised learning with sparsely available labeled samples. Comput. Geosci. 106: 33-48 (2017) - [j11]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse probit linear mixed model. Mach. Learn. 106(9-10): 1621-1642 (2017) - [j10]Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller:
Efficient Exact Inference With Loss Augmented Objective in Structured Learning. IEEE Trans. Neural Networks Learn. Syst. 28(11): 2566-2579 (2017) - 2015
- [j9]Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. 16: 3757-3811 (2015) - 2014
- [j8]S. Derin Babacan, Shinichi Nakajima, Minh N. Do:
Bayesian Group-Sparse Modeling and Variational Inference. IEEE Trans. Signal Process. 62(11): 2906-2921 (2014) - 2013
- [j7]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka:
Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. 14(1): 1-37 (2013) - [j6]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Variational Bayesian sparse additive matrix factorization. Mach. Learn. 92(2-3): 319-347 (2013) - 2011
- [j5]Shinichi Nakajima, Masashi Sugiyama:
Theoretical Analysis of Bayesian Matrix Factorization. J. Mach. Learn. Res. 12: 2583-2648 (2011) - 2010
- [j4]Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese:
Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach. Learn. 78(1-2): 35-61 (2010) - 2009
- [j3]Masashi Sugiyama, Shinichi Nakajima:
Pool-based active learning in approximate linear regression. Mach. Learn. 75(3): 249-274 (2009) - 2007
- [j2]Shinichi Nakajima, Sumio Watanabe:
Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance. Neural Comput. 19(4): 1112-1153 (2007) - 2006
- [j1]Shinichi Nakajima, Sumio Watanabe:
Generalization Performance of Subspace Bayes Approach in Linear Neural Networks. IEICE Trans. Inf. Syst. 89-D(3): 1128-1138 (2006)
Conference and Workshop Papers
- 2023
- [c39]Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Relevant Walk Search for Explaining Graph Neural Networks. ICML 2023: 38301-38324 - [c38]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. NeurIPS 2023 - [c37]Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima:
Physics-Informed Bayesian Optimization of Variational Quantum Circuits. NeurIPS 2023 - 2022
- [c36]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI 2022: 6132-6140 - [c35]Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel:
Path-Gradient Estimators for Continuous Normalizing Flows. ICML 2022: 21945-21959 - [c34]Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. ICML 2022: 24478-24495 - 2020
- [c33]Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance. AAAI 2020: 5842-5850 - [c32]Susanna Schwarzmann, Clarissa Cassales Marquezan, Riccardo Trivisonno, Shinichi Nakajima, Thomas Zinner:
Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks. ICC 2020: 1-6 - [c31]Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin:
Towards Best Practice in Explaining Neural Network Decisions with LRP. IJCNN 2020: 1-7 - 2019
- [c30]Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. AISTATS 2019: 1696-1703 - [c29]Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. EUSIPCO 2019: 1-5 - 2017
- [c28]Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima:
Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshops 2017: 1792-1799 - [c27]János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz:
Minimizing Trust Leaks for Robust Sybil Detection. ICML 2017: 1520-1528 - 2016
- [c26]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft:
Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI 2016: 48-58 - 2014
- [c25]Shinichi Nakajima, Masashi Sugiyama:
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS 2014: 20-28 - [c24]Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi:
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. NIPS 2014: 1224-1232 - 2013
- [c23]Pablo Ruiz, Javier Mateos, María C. Cárdenas, Shinichi Nakajima, Rafael Molina, Aggelos K. Katsaggelos:
Light field acquisition from blurred observations using a programmable coded aperture camera. EUSIPCO 2013: 1-5 - [c22]Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima:
Parametric Task Learning. NIPS 2013: 1358-1366 - [c21]Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi:
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS 2013: 1439-1447 - 2012
- [c20]Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS 2012: 980-988 - [c19]S. Derin Babacan, Shinichi Nakajima, Minh N. Do:
Probabilistic Low-Rank Subspace Clustering. NIPS 2012: 2753-2761 - [c18]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. ACML 2012: 301-316 - 2011
- [c17]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. ICML 2011: 497-504 - [c16]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. NIPS 2011: 208-216 - [c15]Takeshi Matsuo, Shinichi Nakajima:
Attribute-Based MED System with Word Histograms. TRECVID 2011 - 2010
- [c14]Shinichi Nakajima, Masashi Sugiyama:
Implicit Regularization in Variational Bayesian Matrix Factorization. ICML 2010: 815-822 - [c13]Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka:
Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS 2010: 1768-1776 - [c12]Takeshi Matsuo, Shinichi Nakajima:
Nikon Multimedia Event Detection System. TRECVID 2010 - 2009
- [c11]Motoaki Kawanabe, Shinichi Nakajima, Alexander Binder:
A procedure of adaptive kernel combination with kernel-target alignment for object classification. CIVR 2009 - [c10]Nils Plath, Marc Toussaint, Shinichi Nakajima:
Multi-class image segmentation using conditional random fields and global classification. ICML 2009: 817-824 - [c9]Shinichi Nakajima, Masashi Sugiyama:
Analysis of Variational Bayesian Matrix Factorization. PAKDD 2009: 314-326 - [c8]Marius Kloft, Shinichi Nakajima, Ulf Brefeld:
Feature Selection for Density Level-Sets. ECML/PKDD (1) 2009: 692-704 - 2008
- [c7]Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese:
Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. PAKDD 2008: 333-344 - [c6]Masashi Sugiyama, Shinichi Nakajima:
Pool-Based Agnostic Experiment Design in Linear Regression. ECML/PKDD (2) 2008: 406-422 - 2007
- [c5]Shinichi Nakajima, Sumio Watanabe:
Generalization Error of Automatic Relevance Determination. ICANN (1) 2007: 1-10 - [c4]Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe:
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. NIPS 2007: 1433-1440 - 2006
- [c3]Shinichi Nakajima, Sumio Watanabe:
Analytic Solution of Hierarchical Variational Bayes in Linear Inverse Problem. ICANN (2) 2006: 240-249 - [c2]Shingo Takamatsu, Shinichi Nakajima, Sumio Watanabe:
Localized Bayes Estimation for Non-identifiable Models. ICONIP (1) 2006: 650-659 - 2005
- [c1]Shinichi Nakajima, Sumio Watanabe:
Generalization Error of Linear Neural Networks in an Empirical Bayes Approach. IJCAI 2005: 804-810
Informal and Other Publications
- 2024
- [i33]Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima:
Solution Simplex Clustering for Heterogeneous Federated Learning. CoRR abs/2403.03333 (2024) - 2023
- [i32]Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima:
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. CoRR abs/2302.14082 (2023) - [i31]Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek:
Generative Fractional Diffusion Models. CoRR abs/2310.17638 (2023) - [i30]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. CoRR abs/2311.13594 (2023) - 2022
- [i29]Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne:
Visualizing the diversity of representations learned by Bayesian neural networks. CoRR abs/2201.10859 (2022) - [i28]Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima:
Mixture-of-experts VAEs can disregard variation in surjective multimodal data. CoRR abs/2204.05229 (2022) - [i27]Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel:
Path-Gradient Estimators for Continuous Normalizing Flows. CoRR abs/2206.09016 (2022) - [i26]Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel:
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows. CoRR abs/2207.08219 (2022) - [i25]Stephanie Brandl, David Lassner, Anne Baillot, Shinichi Nakajima:
Domain-Specific Word Embeddings with Structure Prediction. CoRR abs/2210.04962 (2022) - 2021
- [i24]Danny Panknin, Shinichi Nakajima, Klaus-Robert Müller:
Optimal Sampling Density for Nonparametric Regression. CoRR abs/2105.11990 (2021) - [i23]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR abs/2106.10185 (2021) - [i22]Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft:
Explaining Bayesian Neural Networks. CoRR abs/2108.10346 (2021) - [i21]Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati:
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. CoRR abs/2111.11303 (2021) - 2020
- [i20]David Lassner, Anne Baillot, Sergej Dogadov, Klaus-Robert Müller, Shinichi Nakajima:
Automatic Identification of Types of Alterations in Historical Manuscripts. CoRR abs/2003.09136 (2020) - [i19]Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon:
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. CoRR abs/2006.03589 (2020) - [i18]Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft:
How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR abs/2006.09000 (2020) - [i17]Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati:
On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. CoRR abs/2007.07115 (2020) - [i16]Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Langevin Cooling for Domain Translation. CoRR abs/2008.13723 (2020) - 2019
- [i15]Danny Panknin, Shinichi Nakajima, Thanh Binh Bui, Klaus-Robert Müller:
Local Bandwidth Estimation via Mixture of Gaussian Processes. CoRR abs/1902.10664 (2019) - [i14]Kim Nicoli, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Shinichi Nakajima:
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. CoRR abs/1903.11048 (2019) - [i13]Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. CoRR abs/1904.05586 (2019) - [i12]Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin:
Towards best practice in explaining neural network decisions with LRP. CoRR abs/1910.09840 (2019) - [i11]Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Pan Kessel:
Asymptotically Unbiased Generative Neural Sampling. CoRR abs/1910.13496 (2019) - [i10]Alexander Bauer, Shinichi Nakajima:
Worst-Case Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies. CoRR abs/1912.12090 (2019) - 2018
- [i9]Vignesh Srinivasan, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima:
Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder. CoRR abs/1805.12017 (2018) - [i8]Hannah Marienwald, Wikor Pronobis, Klaus-Robert Müller, Shinichi Nakajima:
Tight Bound of Incremental Cover Trees for Dynamic Diversification. CoRR abs/1806.06126 (2018) - [i7]Jacob R. Kauffmann, Grégoire Montavon, Luiz Alberto Lima, Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz:
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies. CoRR abs/1806.11326 (2018) - 2017
- [i6]Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. CoRR abs/1708.03314 (2017) - [i5]Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller:
Optimizing for Measure of Performance in Max-Margin Parsing. CoRR abs/1709.01562 (2017) - 2016
- [i4]Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn, Sven Schmeier, Nico Görnitz, Feiyu Xu:
SynsetRank: Degree-adjusted Random Walk for Relation Identification. CoRR abs/1609.00626 (2016) - [i3]Wikor Pronobis, Danny Panknin, Johannes Kirschnick, Vignesh Srinivasan, Wojciech Samek, Volker Markl, Manohar Kaul, Klaus-Robert Müller, Shinichi Nakajima:
Sharing Hash Codes for Multiple Purposes. CoRR abs/1609.03219 (2016) - 2015
- [i2]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse Estimation in a Correlated Probit Model. CoRR abs/1507.04777 (2015) - 2011
- [i1]Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe:
Insights from Classifying Visual Concepts with Multiple Kernel Learning. CoRR abs/1112.3697 (2011)
Coauthor Index
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