Shinichi Nakajima

117 papers A* 16A 2B 3C 1Journal 73Unranked 22

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond the final layer: Attentive multilayer fusion for vision transformers. CoRR Laure Ciernik, Marco Morik, Lukas Thede, Luca Eyring, Shinichi Nakajima, Zeynep Akata, Lukas Muttenthaler
2026	J	`jnl`	Enhancing Brain Source Reconstruction by Initializing 3-D Neural Networks With Physical Inverse Solutions. IEEE Trans. Medical Imaging Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe, Shinichi Nakajima
2025	J	`jnl`	Adaptive Observation Cost Control for Variational Quantum Eigensolvers. CoRR Christopher J. Anders, Kim A. Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima
2025	J	`jnl`	Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers. CoRR Samuele Pedrielli, Christopher J. Anders, Lena Funcke, Karl Jansen, Kim A. Nicoli, Shinichi Nakajima
2025	J	`jnl`	Data augmentation for forecasting industrial aging processes via conditional multimodal generative time-series models. Comput. Chem. Eng. Mihail Bogojeski, Nataliya Yakut, Sasho Nedelkoski, Shinichi Nakajima, Klaus-Robert Müller
2025	J	`jnl`	Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise Ratio. CoRR Khaled Kahouli, Winfried Ripken, Stefan Gugler, Oliver T. Unke, Klaus-Robert Müller, Shinichi Nakajima
2025	J	`jnl`	Explaining Bayesian Neural Networks. Trans. Mach. Learn. Res. Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft
2025	A*	`conf`	Multilevel Generative Samplers for Investigating Critical Phenomena. ICLR Ankur Singha, Elia Cellini, Kim Andrea Nicoli, Karl Jansen, Stefan Kühn, Shinichi Nakajima
2025	J	`jnl`	Multilevel Generative Samplers for Investigating Critical Phenomena. CoRR Ankur Singha, Elia Cellini, Kim A. Nicoli, Karl Jansen, Stefan Kühn, Shinichi Nakajima
2025	J	`jnl`	Towards symbolic XAI - explanation through human understandable logical relationships between features. Inf. Fusion Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, Ping Xiong, Shinichi Nakajima, Stefan Gugler, Grégoire Montavon, Klaus-Robert Müller
2025	—	`conf`	Uncovering the Structure of Explanation Quality with Spectral Analysis. xAI (3) Johannes Maeß, Grégoire Montavon, Shinichi Nakajima, Klaus-Robert Müller, Thomas Schnake
2025	J	`jnl`	Uncovering the Structure of Explanation Quality with Spectral Analysis. CoRR Johannes Maeß, Grégoire Montavon, Shinichi Nakajima, Klaus-Robert Müller, Thomas Schnake
2024	A*	`conf`	Adaptive Observation Cost Control for Variational Quantum Eigensolvers. ICML Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima
2024	J	`jnl`	Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks. CoRR Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe, Shinichi Nakajima
2024	A*	`conf`	Federated Learning over Connected Modes. NeurIPS Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
2024	J	`jnl`	Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects. CoRR Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero
2024	A*	`conf`	Generative Fractional Diffusion Models. NeurIPS Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek
2024	J	`jnl`	Molecular relaxation by reverse diffusion with time step prediction. CoRR Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler, Niklas W. A. Gebauer
2024	J	`jnl`	Molecular relaxation by reverse diffusion with time step prediction. Mach. Learn. Sci. Technol. Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler, Niklas W. A. Gebauer
2024	J	`jnl`	Physics-Informed Bayesian Optimization of Variational Quantum Circuits. CoRR Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
2024	J	`jnl`	Solution Simplex Clustering for Heterogeneous Federated Learning. CoRR Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
2024	J	`jnl`	Towards Symbolic XAI - Explanation Through Human Understandable Logical Relationships Between Features. CoRR Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, Ping Xiong, Shinichi Nakajima, Stefan Gugler, Grégoire Montavon, Klaus-Robert Müller
2023	J	`jnl`	Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. CoRR Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima
2023	J	`jnl`	Domain-Specific Word Embeddings with Structure Prediction. Trans. Assoc. Comput. Linguistics David Lassner, Stephanie Brandl, Anne Baillot, Shinichi Nakajima
2023	J	`jnl`	Generative Fractional Diffusion Models. CoRR Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek
2023	A*	`conf`	Labeling Neural Representations with Inverse Recognition. NeurIPS Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
2023	J	`jnl`	Labeling Neural Representations with Inverse Recognition. CoRR Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
2023	J	`jnl`	Langevin Cooling for Unsupervised Domain Translation. IEEE Trans. Neural Networks Learn. Syst. Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2023	J	`jnl`	Local Function Complexity for Active Learning via Mixture of Gaussian Processes. Trans. Mach. Learn. Res. Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima
2023	A*	`conf`	Physics-Informed Bayesian Optimization of Variational Quantum Circuits. NeurIPS Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
2023	A*	`conf`	Relevant Walk Search for Explaining Graph Neural Networks. ICML Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima
2023	J	`jnl`	Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Trans. Mach. Learn. Res. Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
2022	J	`jnl`	Domain-Specific Word Embeddings with Structure Prediction. CoRR Stephanie Brandl, David Lassner, Anne Baillot, Shinichi Nakajima
2022	A*	`conf`	Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. ICML Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima
2022	J	`jnl`	Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows. CoRR Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel
2022	J	`jnl`	Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. Mach. Learn. Sci. Technol. Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel
2022	J	`jnl`	Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Trans. Pattern Anal. Mach. Intell. Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
2022	J	`jnl`	ML-Based QoE Estimation in 5G Networks Using Different Regression Techniques. IEEE Trans. Netw. Serv. Manag. Susanna Schwarzmann, Clarissa Cassales Marquezan, Riccardo Trivisonno, Shinichi Nakajima, Vincent Barriac, Thomas Zinner
2022	J	`jnl`	Mixture-of-experts VAEs can disregard variation in surjective multimodal data. CoRR Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima
2022	A*	`conf`	NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
2022	A*	`conf`	Path-Gradient Estimators for Continuous Normalizing Flows. ICML Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel
2022	J	`jnl`	Path-Gradient Estimators for Continuous Normalizing Flows. CoRR Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel
2022	J	`jnl`	Visualizing the diversity of representations learned by Bayesian neural networks. CoRR Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
2021	J	`jnl`	Explaining Bayesian Neural Networks. CoRR Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft
2021	J	`jnl`	Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. CoRR Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
2021	J	`jnl`	NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
2021	J	`jnl`	Optimal Sampling Density for Nonparametric Regression. CoRR Danny Panknin, Shinichi Nakajima, Klaus-Robert Müller
2021	J	`jnl`	Robustifying models against adversarial attacks by Langevin dynamics. Neural Networks Vignesh Srinivasan, Csaba Rohrer, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2020	—	`conf`	Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks. ICC Susanna Schwarzmann, Clarissa Cassales Marquezan, Riccardo Trivisonno, Shinichi Nakajima, Thomas Zinner
2020	J	`jnl`	Automatic Identification of Types of Alterations in Historical Manuscripts. CoRR David Lassner, Anne Baillot, Sergej Dogadov, Klaus-Robert Müller, Shinichi Nakajima
2020	A*	`conf`	Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance. AAAI Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2020	J	`jnl`	How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft
2020	J	`jnl`	Langevin Cooling for Domain Translation. CoRR Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2020	J	`jnl`	On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. CoRR Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
2020	J	`jnl`	Optimizing for Measure of Performance in Max-Margin Parsing. IEEE Trans. Neural Networks Learn. Syst. Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
2020	B	`conf`	Towards Best Practice in Explaining Neural Network Decisions with LRP. IJCNN Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
2020	J	`jnl`	XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. CoRR Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
2019	J	`jnl`	Asymptotically Unbiased Generative Neural Sampling. CoRR Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Pan Kessel
2019	—	`conf`	Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. EUSIPCO Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2019	J	`jnl`	Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. CoRR Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2019	J	`jnl`	Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. CoRR Kim Nicoli, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Shinichi Nakajima
2019	J	`jnl`	Local Bandwidth Estimation via Mixture of Gaussian Processes. CoRR Danny Panknin, Shinichi Nakajima, Thanh Binh Bui, Klaus-Robert Müller
2019	A	`conf`	Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. AISTATS Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
2019	J	`jnl`	Towards best practice in explaining neural network decisions with LRP. CoRR Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
2019	J	`jnl`	Worst-Case Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies. CoRR Alexander Bauer, Shinichi Nakajima
2018	J	`jnl`	Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder. CoRR Vignesh Srinivasan, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
2018	J	`jnl`	Support Vector Data Descriptions and k-Means Clustering: One Class? IEEE Trans. Neural Networks Learn. Syst. Nico Görnitz, Luiz Alberto Lima, Klaus-Robert Müller, Marius Kloft, Shinichi Nakajima
2018	J	`jnl`	Tight Bound of Incremental Cover Trees for Dynamic Diversification. CoRR Hannah Marienwald, Wikor Pronobis, Klaus-Robert Müller, Shinichi Nakajima
2018	J	`jnl`	Transductive Regression for Data With Latent Dependence Structure. IEEE Trans. Neural Networks Learn. Syst. Nico Görnitz, Luiz Alberto Lima, Luiz Eduardo Varella, Klaus-Robert Müller, Shinichi Nakajima
2018	J	`jnl`	Unsupervised Detection and Explanation of Latent-class Contextual Anomalies. CoRR Jacob R. Kauffmann, Grégoire Montavon, Luiz Alberto Lima, Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz
2018	J	`jnl`	Wasserstein Stationary Subspace Analysis. IEEE J. Sel. Top. Signal Process. Stephan Kaltenstadler, Shinichi Nakajima, Klaus-Robert Müller, Wojciech Samek
2017	J	`jnl`	Efficient Exact Inference With Loss Augmented Objective in Structured Learning. IEEE Trans. Neural Networks Learn. Syst. Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller
2017	A*	`conf`	Minimizing Trust Leaks for Robust Sybil Detection. ICML János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz
2017	J	`jnl`	Optimizing for Measure of Performance in Max-Margin Parsing. CoRR Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
2017	J	`jnl`	Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. CoRR Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
2017	J	`jnl`	Porosity estimation by semi-supervised learning with sparsely available labeled samples. Comput. Geosci. Luiz Alberto Lima, Nico Görnitz, Luiz Eduardo Varella, Marley M. B. R. Vellasco, Klaus-Robert Müller, Shinichi Nakajima
2017	J	`jnl`	Sparse probit linear mixed model. Mach. Learn. Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
2017	—	`conf`	Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshops Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima
2016	—	`conf`	Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft
2016	J	`jnl`	Sharing Hash Codes for Multiple Purposes. CoRR Wikor Pronobis, Danny Panknin, Johannes Kirschnick, Vignesh Srinivasan, Wojciech Samek, Volker Markl, Manohar Kaul, Klaus-Robert Müller, Shinichi Nakajima
2016	J	`jnl`	SynsetRank: Degree-adjusted Random Walk for Relation Identification. CoRR Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn, Sven Schmeier, Nico Görnitz, Feiyu Xu
2015	J	`jnl`	Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
2015	J	`jnl`	Sparse Estimation in a Correlated Probit Model. CoRR Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
2014	A	`conf`	Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS Shinichi Nakajima, Masashi Sugiyama
2014	—	`conf`	Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. NIPS Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
2014	J	`jnl`	Bayesian Group-Sparse Modeling and Variational Inference. IEEE Trans. Signal Process. S. Derin Babacan, Shinichi Nakajima, Minh N. Do
2013	—	`conf`	Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
2013	J	`jnl`	Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka
2013	—	`conf`	Light field acquisition from blurred observations using a programmable coded aperture camera. EUSIPCO Pablo Ruiz, Javier Mateos, María C. Cárdenas, Shinichi Nakajima, Rafael Molina, Aggelos K. Katsaggelos
2013	—	`conf`	Parametric Task Learning. NIPS Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
2013	J	`jnl`	Variational Bayesian sparse additive matrix factorization. Mach. Learn. Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2012	—	`conf`	Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
2012	—	`conf`	Probabilistic Low-Rank Subspace Clustering. NIPS S. Derin Babacan, Shinichi Nakajima, Minh N. Do
2012	C	`conf`	Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. ACML Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2011	—	`conf`	Attribute-Based MED System with Word Histograms. TRECVID Takeshi Matsuo, Shinichi Nakajima
2011	—	`conf`	Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. NIPS Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2011	J	`jnl`	Insights from Classifying Visual Concepts with Multiple Kernel Learning CoRR Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe
2011	A*	`conf`	On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. ICML Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
2011	J	`jnl`	Theoretical Analysis of Bayesian Matrix Factorization. J. Mach. Learn. Res. Shinichi Nakajima, Masashi Sugiyama
2010	—	`conf`	Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka
2010	A*	`conf`	Implicit Regularization in Variational Bayesian Matrix Factorization. ICML Shinichi Nakajima, Masashi Sugiyama
2010	—	`conf`	Nikon Multimedia Event Detection System. TRECVID Takeshi Matsuo, Shinichi Nakajima
2010	J	`jnl`	Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach. Learn. Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
2009	—	`conf`	A procedure of adaptive kernel combination with kernel-target alignment for object classification. CIVR Motoaki Kawanabe, Shinichi Nakajima, Alexander Binder
2009	B	`conf`	Analysis of Variational Bayesian Matrix Factorization. PAKDD Shinichi Nakajima, Masashi Sugiyama
2009	—	`conf`	Feature Selection for Density Level-Sets. ECML/PKDD (1) Marius Kloft, Shinichi Nakajima, Ulf Brefeld
2009	A*	`conf`	Multi-class image segmentation using conditional random fields and global classification. ICML Nils Plath, Marc Toussaint, Shinichi Nakajima
2009	J	`jnl`	Pool-based active learning in approximate linear regression. Mach. Learn. Masashi Sugiyama, Shinichi Nakajima
2008	—	`conf`	Pool-Based Agnostic Experiment Design in Linear Regression. ECML/PKDD (2) Masashi Sugiyama, Shinichi Nakajima
2008	B	`conf`	Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. PAKDD Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
2007	—	`conf`	Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. NIPS Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe
2007	—	`conf`	Generalization Error of Automatic Relevance Determination. ICANN (1) Shinichi Nakajima, Sumio Watanabe
2007	J	`jnl`	Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance. Neural Comput. Shinichi Nakajima, Sumio Watanabe
2006	—	`conf`	Analytic Solution of Hierarchical Variational Bayes in Linear Inverse Problem. ICANN (2) Shinichi Nakajima, Sumio Watanabe
2006	J	`jnl`	Generalization Performance of Subspace Bayes Approach in Linear Neural Networks. IEICE Trans. Inf. Syst. Shinichi Nakajima, Sumio Watanabe
2006	—	`conf`	Localized Bayes Estimation for Non-identifiable Models. ICONIP (1) Shingo Takamatsu, Shinichi Nakajima, Sumio Watanabe
2005	A*	`conf`	Generalization Error of Linear Neural Networks in an Empirical Bayes Approach. IJCAI Shinichi Nakajima, Sumio Watanabe