Hisashi Kashima

Artificial Intelligence · 2003–2019

A researcher in the Science corpus: 13 papers; trusts 89 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #14,102 of 35,345 by trust here, #8,810 by citations
• Molecular Biology — 39 citations into it; #13,385 of 17,773 by trust here, #13,891 by citations
• Reinforcement Learning — 32 citations into it; #1,323 of 2,242 by trust here, #1,455 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #11,933 of 21,575 by trust here, #9,722 by citations
• Safety, Robustness & Interpretability — 28 citations into it
• Statistics and Probability — 26 citations into it
• Statistical Learning Theory — 24 citations into it; #582 of 1,038 by trust here, #256 by citations
• Representation & Self-Supervised Learning — 24 citations into it; #2,226 of 3,145 by trust here, #1,913 by citations

Whom Hisashi Kashima trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David Haussler
2. Richard S. Sutton
3. Bernhard Schölkopf
4. Andrew Barto
5. Yoav Freund
6. Michael Collins
7. Alex Smola
8. Geoffrey E. Hinton
9. L. Eon Bottou
10. Alex Krizhevsky

Who trusts Hisashi Kashima overall

1. Masashi Sugiyama
2. Karsten M. Borgwardt
3. Yu Zhang
4. Thomas Gärtner
5. Taiji Suzuki
6. Qiang Yang
7. Yu Zhang
8. Wouter M. Kouw
9. Nils M. Kriege
10. Charu C. Aggarwal

Papers from Hisashi Kashima's perspective

Ordered by how strongly the researchers Hisashi Kashima trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Parametric Return Density Estimation for Reinforcement Learning (2010) — 120 citations
• Direct importance estimation for covariate shift adaptation (2008) — 498 citations
• Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family (2019) — 91 citations
• Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation (2009) — 173 citations
• Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem (2015) — 112 citations
• A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference (2010) — 43 citations
• Distributed Multi-task Learning for Sensor Network (2017) — 7 citations
• Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning (2013) — 60 citations
• Nonparametric Return Distribution Approximation for Reinforcement Learning (2010) — 125 citations
• KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways (2009) — 68 citations
• Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation (2007) — 962 citations
• Multi-Task Learning via Conic Programming (2007) — 113 citations
• Marginalized Kernels Between Labeled Graphs (2003) — 898 citations