Akiyoshi Sannai

According to our database, Akiyoshi Sannai authored at least 23 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Discovering New Theorems via LLMs with In-Context Proof Learning in Lean.

Kazumi Kasaura, Naoto Onda, Yuta Oriike, Masaya Taniguchi, Akiyoshi Sannai, Sho Sonoda

CoRR, September, 2025

LeanConjecturer: Automatic Generation of Mathematical Conjectures for Theorem Proving.

Naoto Onda, Kazumi Kasaura, Yuta Oriike, Masaya Taniguchi, Akiyoshi Sannai, Sho Sonoda

CoRR, June, 2025

Prover Agent: An Agent-based Framework for Formal Mathematical Proofs.

Kaito Baba, Chaoran Liu, Shuhei Kurita, Akiyoshi Sannai

CoRR, June, 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

Trans. Mach. Learn. Res., 2025

Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization.

Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada

Trans. Mach. Learn. Res., 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Universal approximation with neural networks on function spaces.

Wataru Kumagai, Akiyoshi Sannai, Makoto Kawano

J. Exp. Theor. Artif. Intell., October, 2024

Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry.

Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier

Trans. Mach. Learn. Res., 2024

Invariant and Equivariant Reynolds Networks.

Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai

J. Mach. Learn. Res., 2024

Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE.

Koji Hashimoto, Yuji Hirono, Akiyoshi Sannai

CoRR, 2024

A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees.

Toshinori Kitamura, Tadashi Kozuno, Masahiro Kato, Yuki Ichihara, Soichiro Nishimori, Akiyoshi Sannai, Sho Sonoda, Wataru Kumagai, Yutaka Matsuo

CoRR, 2024

2023

LPML: LLM-Prompting Markup Language for Mathematical Reasoning.

Ryutaro Yamauchi, Sho Sonoda, Akiyoshi Sannai, Wataru Kumagai

CoRR, 2023

2021

Equivariant and Invariant Reynolds Networks.

Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai

CoRR, 2021

Approximate Bayesian Computation of Bézier Simplices.

Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

CoRR, 2021

Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces.

Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Group Equivariant Conditional Neural Processes.

Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Universal Approximation Theorem for Equivariant Maps by Group CNNs.

Wataru Kumagai, Akiyoshi Sannai

CoRR, 2020

Asymptotic Risk of Bézier Simplex Fitting.

Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Improved Generalization Bound of Permutation Invariant Deep Neural Networks.

Akiyoshi Sannai, Masaaki Imaizumi

CoRR, 2019

Universal approximations of permutation invariant/equivariant functions by deep neural networks.

Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier

CoRR, 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

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Reconstruction of training samples from loss functions.

Akiyoshi Sannai

CoRR, 2018