Nakajima, Shinichi

42 publications

TMLR 2025 Explaining Bayesian Neural Networks Kirill Bykov, Marina MC Höhne, Adelaida Creosteanu, Klaus Robert Muller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft

ICLR 2025 Multilevel Generative Samplers for Investigating Critical Phenomena Ankur Singha, Elia Cellini, Kim Andrea Nicoli, Karl Jansen, Stefan Kühn, Shinichi Nakajima

ICML 2024 Adaptive Observation Cost Control for Variational Quantum Eigensolvers Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima

NeurIPS 2024 Federated Learning over Connected Modes Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima

NeurIPS 2024 Generative Fractional Diffusion Models 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

ICMLW 2024 Generative Fractional Diffusion Models 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

NeurIPS 2023 Labeling Neural Representations with Inverse Recognition Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina Höhne

TMLR 2023 Local Function Complexity for Active Learning via Mixture of Gaussian Processes Danny Panknin, Stefan Chmiela, Klaus Robert Muller, Shinichi Nakajima

NeurIPS 2023 Physics-Informed Bayesian Optimization of Variational Quantum Circuits Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima

ICML 2023 Relevant Walk Search for Explaining Graph Neural Networks Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus Robert Muller, Shinichi Nakajima

TMLR 2023 Visualizing the Diversity of Representations Learned by Bayesian Neural Networks Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina MC Höhne

ICML 2022 Efficient Computation of Higher-Order Subgraph Attribution via Message Passing Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima

AAAI 2022 NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne

ICML 2022 Path-Gradient Estimators for Continuous Normalizing Flows Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel

AAAI 2020 Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima

AISTATS 2019 Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs Alexander Bauer, Shinichi Nakajima, Nico Goernitz, Klaus-Robert Müller

ICML 2017 Minimizing Trust Leaks for Robust Sybil Detection János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz

MLJ 2017 Sparse Probit Linear Mixed Model Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft

ICCVW 2017 Variational Robust Subspace Clustering with Mean Update Algorithm Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima

UAI 2016 Separating Sparse Signals from Correlated Noise in Binary Classification Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft

JMLR 2015 Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan

AISTATS 2014 Analysis of Empirical MAP and Empirical Partially Bayes: Can They Be Alternatives to Variational Bayes? Shinichi Nakajima, Masashi Sugiyama

NeurIPS 2014 Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity than MAP Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi

JMLR 2013 Global Analytic Solution of Fully-Observed Variational Bayesian Matrix Factorization Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka

NeurIPS 2013 Global Solver and Its Efficient Approximation for Variational Bayesian Low-Rank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

NeurIPS 2013 Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima

MLJ 2013 Variational Bayesian Sparse Additive Matrix Factorization Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan

NeurIPS 2012 Perfect Dimensionality Recovery by Variational Bayesian PCA Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. D. Babacan

NeurIPS 2012 Probabilistic Low-Rank Subspace Clustering S. D. Babacan, Shinichi Nakajima, Minh Do

ACML 2012 Sparse Additive Matrix Factorization for Robust PCA and Its Generalization Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan

NeurIPS 2011 Global Solution of Fully-Observed Variational Bayesian Matrix Factorization Is Column-Wise Independent Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan

ICML 2011 On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan

JMLR 2011 Theoretical Analysis of Bayesian Matrix Factorization Shinichi Nakajima, Masashi Sugiyama

NeurIPS 2010 Global Analytic Solution for Variational Bayesian Matrix Factorization Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka

ICML 2010 Implicit Regularization in Variational Bayesian Matrix Factorization Shinichi Nakajima, Masashi Sugiyama

MLJ 2010 Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese

ECML-PKDD 2009 Feature Selection for Density Level-Sets Marius Kloft, Shinichi Nakajima, Ulf Brefeld

ICML 2009 Multi-Class Image Segmentation Using Conditional Random Fields and Global Classification Nils Plath, Marc Toussaint, Shinichi Nakajima

MLJ 2009 Pool-Based Active Learning in Approximate Linear Regression Masashi Sugiyama, Shinichi Nakajima

ECML-PKDD 2008 Pool-Based Agnostic Experiment Design in Linear Regression Masashi Sugiyama, Shinichi Nakajima

NeurIPS 2007 Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V. Buenau, Motoaki Kawanabe

IJCAI 2005 Generalization Error of Linear Neural Networks in an Empirical Bayes Approach Shinichi Nakajima, Sumio Watanabe