Sannai, Akiyoshi

10 publications

TMLR 2025 Bézier Flow: A Surface-Wise Gradient Descent Method for Multi-Objective Optimization Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada

TMLR 2025 Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai

AISTATS 2025 Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Akiyoshi Sannai, Naoki Hamada

TMLR 2024 Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation Under Symmetry. Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier

MLOSS 2024 Invariant and Equivariant Reynolds Networks Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai

AISTATS 2021 On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity Yuuki Takai, Akiyoshi Sannai, Matthieu Cordonnier

ICLR 2021 Group Equivariant Conditional Neural Processes Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo

UAI 2021 Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano

AAAI 2020 Asymptotic Risk of Bézier Simplex Fitting Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

AAAI 2019 Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama