Conference Talks

Kobayashi, K.; Data analysis focusing on geodesic distance and curvature, Algebraic Statistics 2022, University of Hawai'i at Manoa, Honolulu, 2022.5.16 (keynote speech).

Kamoi, R. and Kobayashi, K.: Out-of-Distribution Detection with Likelihoods Assigned by Deep Generative Models Using Multimodal Prior Distributions, The AAAI’s Workshop on Artificial Intelligence Safety (SafeAI 2020), New York, 2020.2.7(poster)

Kobayashi, K.: Statistical inference and data analysis on length metric spaces, 32nd European Meeting of Statisticians, Palermo, 2019.7.23.

Kobayashi, K. and Wynn, H.: Element-wise shrinkage of positive correlation matrices for increasing sparsity, 2018 IMS Annual Meeting on Probability and Statistics, Vilnius, Lithuania, 2018.07.03 (poster)

Kobayashi, K. and Wynn, H.: A new aspect of geometrical data analysis using curvature of the data space and the empirical graph, Royal Statistical Society Conference 2017, Glasgow, 2017.9.5.(poster)

Kobayashi, K.: Curvatures for data spaces and their empirical graphs, Algebraic and Geometric Methods in Statistics, Institute of Statistical Mathematics, Tachikawa, 2017.3.7.

Kobayashi, K.: Statistical analysis by tuning curvature of data spaces, Boston-Keio Workshop, Boston Univeristy, 2016.8.16.

Kobayashi, K.: Data analysis using curvature of data spaces and their metric cones, The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting(IMS-APRM2016), The Chinese University of Hong Kong, 2016.6.28.

Kobayashi, K.: Generating statistically efficient estimators via computational algebra, Application of Algebraic Methods to Statistics, RIMS, Kyoto University, 2016.6.23 (invited)

Hara, K., Suzuki, I., Kobayashi, K., Fukumizu, K. and Radovanovic, M.: Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness, 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, USA, 2016.2.15(poster)

Kobayashi, K. and Orita, M.: Geometry of dendrogram space and its application to mental lexicon analysis, 6th International Conference on Applied Physics and Mathematics (ICAPM 2016), Singapore, 2016.1.14.

Hara, K., Suzuki, I., Kobayashi, K. and Fukumizu, K. and Radovanovic, M.: Reducing Hubness for Kernel Regression, SISAP2015, Glasgow, 2015.10.12.(poster)

Shinzato, T., Kaku, I. and Kobayashi, K. (2015), A Discussion on Universality of Makespan in Flow Shop Scheduling Problem, 2015 Asian Conference of Management Science & Applications, Dalian, China, 2015.9.13

Hara, K., Suzuki, I., Kobayashi, K. and Fukumizu, K. (2015), Reducing Hubness: A Cause of Vulnerability in Recommender Systems, In proceedings of the 38th Annual ACM SIGIR Conference, pp. 815-818, Santiago de Chile,2015.8.11.(poster)

Kobayashi, K. and Wynn, H.: Intrinsic and extrinsic means and curvature of metric cones, Algebraic Statistics 2015, Genoa, 2015.6.9 (poster)

Kobayashi, K.: Geodesic distances on data spaces: their computation and modification, ISI-ISM-ISSAS joint Conference 2015, Tokyo, 2015.4.2

Hara, K., Suzuki, I., Shimbo, M., Kobayashi, K., Fukumizu, K. and Radovanovic, M.: Localized Centering: Reducing Hubness in Large-Sample Data, The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, Texias, 2015.1.29.(poster)

Kobayashi, K., Orita, M. and Wynn, H.: Statistical analysis via the curvature of data space, MaxEnt 2014, Amboise, 2014.9.22.

Kobayashi, K. and Wynn, H.: The empirical geodesic graphs and their deformation for data analysis, ASC-IMS 2014, Australian Technology Park, Sydney, 2014.7.9.

Kobayashi, K.: Curvature of empirical metrics on a data space and its deformation, Workshop on Mathematical Approaches to Large-Dimensional Data Analysis, ISM, Tokyo, 2014.3.14.

Kobayashi, K.: Hypothesis Testing for the Difference of Dendrograms, ISI-ISM-ISSAS Joint Conference 2014, ISI, Delhi, 2014.2.20.

Kobayashi, K. and Wynn, H.: The empirical geodesic graphs and a deformation of their metric, Computational Algebraic Statistics, Theories and Applications (CASTA 2014), Kyoto, 2014.1.23.

Kobayashi, K. and Wynn, H.: Asymptotically Efficient Estimators for Algebraic Statistical Manifolds, First International Conference on Geometric Science of Information 2013, Ecole des Mines de Paris, 2013.8.28.

Kobayashi, K.: An algebraic computation method for asymptotically efficient estimators, Joint Meeting of the IASC Satellite Conference and the 8th Conference of the Asian Regional Section of the IASC, Seoul, 2013.8.23 (invited Talk).

Kobayashi, K.: The best upper bound on total variation distance by DeRobertis separation, 8th World Congress in Probability and Statistics, Istanbul, 2013.7.9.

Kobayashi, K.: DeRobertis Separation, Its Application to Bayesian Analysis and Generalization, ISI-ISM-ISSAS Joint Conference, Academia Sinica, Taipei, 2013.2.1.

Kobayashi, K. and Wynn, H.: Asymptotic estimation theory via algebraic　computation, The 2nd Institute of Mathematical Statistics Aisa Pacific Rim Meeting (IMS-APRM), Tsukuba, 2012.7.3.

Orita, M. and Kobayashi, K.: Semantically Equivalent Lexical Items between L1 and L2 Mental Lexicons, 22nd Vocabulary Acquisition Research Group Network Conference, Swansea Univ., 2012.3.16.

Kobayashi, K.: Asymptotic efficiency of statistical estimators via algebraic computation and information geometry, ISM-ISI-ISSAS Joint Conference, ISM, Tokyo, 2012.2.3.

Kobayashi, K. and Wynn, H.: Algebraic computations for asymptotically efficient estimators via information geometry, Workshop on Geometric and Algebraic Statistics 3, University of Warwick, 2011.4.7.

Orita, M. and Kobayashi, K.: Semantic Clustering of High Frequency Nouns in L1 and L2 Mental Lexicons, Learners and Networks Conference 2011, Swansea University, 2011.3.18.

Kobayashi, K. and Orita, M.: Difference in mental lexicon between native and non-native English speakers, 73rd Annual Meeting of the Institute of Mathematical Statistics, Gothenburg, 2010.8.13.

Kobayashi, K. and Wynn, H.: Using algebraic method in information geometry, Information Geometry and its Applications III, Leipzig, 2010.8.2-5.

Orita, M. and Kobayashi, K.: Effects of intra-lexical Features on the completion time of sorting tasks, 20th Vocabulary Acquisition Research Group Network Conference, Gregynog, 2010.3.17-20.

Orita, M. and Kobayashi, K.: Predictors of L1 and L2 differences in lexical organisation, The 6th Vocabulary Acquisition Research Group Conference, Tokyo, 2009.12.5.

Kobayashi, K.: Shrinkage Bayesian prediction and its application to regression problems, Statistics Seminar, Queen Mary Univ. of London, 2009.3.4

Kobayashi, K. and Komaki, F.: Minimaxity of Stein-type Bayesian prediction for normal regression problem, 7th World Congress in Probability and Statistics, Singapore, 2008.07.17.

Kobayashi, K.: A Bayesian prediction for the Normal distributions with changeable covariances, Joint Meeting of ISI, ISM and ISSAS, Taipei, 2008.06.20.

Kobayashi, K. and Komaki, F.: “Shrinkage prediction for the Normal regression problem with Kullback-Leibler loss function”, Proceedings of the Second International Symposium on Information Geometry and its Applications, pp. 237-244, University of Tokyo, 2005.12.15

Kobayashi, K. and Komaki, F.: “Risk sensitive decision networks, Second Latin American Congress on Bayesian Statistics, San Jose del Cabo, 2005.2.9

Kei Kobayashi and Fumiyasu Komaki: “A Bayesian feature selection for Kernel Machines”, East Asian Symposium on Statistics, Seoul, 2002.12.3

Kei Kobayashi and Kokichi Sugihara: “Crystal Voronoi Diagram and Its Applications to Collision-Free Paths”, International Conference on Computational Science (1) , San Francisco, 2001.5.28

See Japanese Page.