Yamanishi, Kenji

27 publications

NeurIPS 2025 Bandit and Delayed Feedback in Online Structured Prediction Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi

NeurIPS 2023 Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi

ICML 2023 Tight and Fast Generalization Error Bound of Graph Embedding in Metric Space Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi

NeurIPS 2021 Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

ICML 2021 Generalization Error Bound for Hyperbolic Ordinal Embedding Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

IJCAI 2020 Discovering Latent Class Labels for Multi-Label Learning Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi

AISTATS 2019 Adaptive Minimax Regret Against Smooth Logarithmic Losses over High-Dimensional L1-Balls via Envelope Complexity Kohei Miyaguchi, Kenji Yamanishi

IJCAI 2019 Attributed Subspace Clustering Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi

ACML 2019 Hyperbolic Ordinal Embedding Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi

AAAI 2019 Orderly Subspace Clustering Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi

MLJ 2018 High-Dimensional Penalty Selection via Minimum Description Length Principle Kohei Miyaguchi, Kenji Yamanishi

IJCAI 2018 Ranking Preserving Nonnegative Matrix Factorization Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi

ALT 1999 Extended Stochastic Complexity and Minimax Relative Loss Analysis Kenji Yamanishi

COLT 1998 Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses Kenji Yamanishi

COLT 1997 Distributed Cooperative Bayesian Learning Strategies Kenji Yamanishi

COLT 1996 A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables Kenji Yamanishi

MLJ 1995 Probably Almost Discriminative Learning Kenji Yamanishi

COLT 1995 Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination Kenji Yamanishi

COLT 1994 The Minimum L-Complexity Algorithm and Its Applications to Learning Non-Parametric Rules Kenji Yamanishi

COLT 1993 On Polynomial-Time Probably Almost Discriminative Learnability Kenji Yamanishi

MLJ 1992 A Learning Criterion for Stochastic Rules Kenji Yamanishi

COLT 1992 Probably Almost Discriminative Learning Kenji Yamanishi

ALT 1992 Protein Secondary Structure Prediction Based on Stochastic-Rule Learning Hiroshi Mamitsuka, Kenji Yamanishi

COLT 1991 A Loss Bound Model for On-Line Stochastic Prediction Strategies Kenji Yamanishi

ALT 1991 Learning Non-Parametric Densities by Finite-Dimensional Parametric Hypotheses Kenji Yamanishi

ICML 1991 Learning Stochastic Motifs from Genetic Sequences Kenji Yamanishi, Akihiko Konagaya

COLT 1990 A Learning Criterion for Stochastic Rules Kenji Yamanishi