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Akifumi Okuno
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2020 – today
- 2024
- [j3]Akifumi Okuno
, Yuya Morishita, Yoh-ichi Mototake:
Autoregressive With Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series. IEEE Access 12: 24621-24630 (2024) - [i14]Akifumi Okuno:
An integrated perspective of robustness in regression through the lens of the bias-variance trade-off. CoRR abs/2407.10418 (2024) - 2023
- [j2]Akifumi Okuno
A Generalization Gap Estimation for Overparameterized Models via the Langevin Functional Variance. J. Comput. Graph. Stat. 32(4): 1287-1295 (2023) - [i13]Akifumi Okuno, Kazuharu Harada:
An interpretable neural network-based non-proportional odds model for ordinal regression with continuous response. CoRR abs/2303.17823 (2023) - [i12]Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake:
Forecasting of the development of a partially-observed dynamical time series with the aid of time-invariance and linearity. CoRR abs/2306.16593 (2023) - [i11]Akifumi Okuno:
A stochastic optimization approach to train non-linear neural networks with a higher-order variation regularization. CoRR abs/2308.02293 (2023) - 2022
- [i10]Akifumi Okuno, Kohei Hattori:
A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates. CoRR abs/2204.08205 (2022) - 2021
- [i9]Akifumi Okuno, Keisuke Yano:
A generalization gap estimation for overparameterized models via Langevin functional variance. CoRR abs/2112.03660 (2021) - [i8]Ruixing Cao, Akifumi Okuno, Kei Nakagawa, Hidetoshi Shimodaira:
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction. CoRR abs/2112.13951 (2021) - 2020
- [b1]Akifumi Okuno:
Studies on Neural Network-Based Graph Embedding and Its Extensions. Kyoto University, Japan, 2020 - [j1]Akifumi Okuno
Hyperlink regression via Bregman divergence. Neural Networks 126: 362-383 (2020) - [c6]Akifumi Okuno, Hidetoshi Shimodaira:
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate. NeurIPS 2020 - [i7]Akifumi Okuno, Hidetoshi Shimodaira:
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate. CoRR abs/2002.03054 (2020) - [i6]Morihiro Mizutani, Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira:
Stochastic Neighbor Embedding of Multimodal Relational Data for Image-Text Simultaneous Visualization. CoRR abs/2005.00670 (2020)
2010 – 2019
- 2019
- [c5]Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira:
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability. AISTATS 2019: 644-653 - [c4]Akifumi Okuno, Hidetoshi Shimodaira:
Robust Graph Embedding with Noisy Link Weights. AISTATS 2019: 664-673 - [c3]Geewook Kim, Akifumi Okuno, Kazuki Fukui, Hidetoshi Shimodaira
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities. IJCAI 2019: 5031-5038 - [i5]Akifumi Okuno, Hidetoshi Shimodaira:
Robust Graph Embedding with Noisy Link Weights. CoRR abs/1902.08440 (2019) - [i4]Geewook Kim, Akifumi Okuno, Kazuki Fukui, Hidetoshi Shimodaira:
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities. CoRR abs/1902.10409 (2019) - [i3]Akifumi Okuno, Hidetoshi Shimodaira:
Hyperlink Regression via Bregman Divergence. CoRR abs/1908.02573 (2019) - 2018
- [c2]Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira:
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks. ICML 2018: 3885-3894 - [i2]Akifumi Okuno, Hidetoshi Shimodaira:
On representation power of neural network-based graph embedding and beyond. CoRR abs/1805.12332 (2018) - [i1]Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira:
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability. CoRR abs/1810.03463 (2018) - 2016
- [c1]Kazuki Fukui, Akifumi Okuno, Hidetoshi Shimodaira
Image and tag retrieval by leveraging image-group links with multi-domain graph embedding. ICIP 2016: 221-225
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