Reconstruction of training samples from loss functions. - Akiyoshi Sannai (CoRR, 2018)

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 (AAAI, 2019)

Universal approximations of permutation invariant/equivariant functions by deep neural networks. - Akiyoshi Sannai; Yuuki Takai; Matthieu Cordonnier (CoRR, 2019)

Improved Generalization Bound of Permutation Invariant Deep Neural Networks. - Akiyoshi Sannai; Masaaki Imaizumi (CoRR, 2019)

Asymptotic Risk of Bézier Simplex Fitting. - Akinori Tanaka; Akiyoshi Sannai; Ken Kobayashi; Naoki Hamada (AAAI, 2020)

Universal Approximation Theorem for Equivariant Maps by Group CNNs. - Wataru Kumagai; Akiyoshi Sannai (CoRR, 2020)

On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity. - Yuuki Takai; Akiyoshi Sannai; Matthieu Cordonnier (AISTATS, 2021)

Group Equivariant Conditional Neural Processes. - Makoto Kawano; Wataru Kumagai; Akiyoshi Sannai; Yusuke Iwasawa; Yutaka Matsuo (ICLR, 2021)

Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces. - Akiyoshi Sannai; Masaaki Imaizumi; Makoto Kawano (UAI, 2021)

Approximate Bayesian Computation of Bézier Simplices. - Akinori Tanaka; Akiyoshi Sannai; Ken Kobayashi; Naoki Hamada (CoRR, 2021)

Equivariant and Invariant Reynolds Networks. - Akiyoshi Sannai; Makoto Kawano; Wataru Kumagai (CoRR, 2021)

LPML: LLM-Prompting Markup Language for Mathematical Reasoning. - Ryutaro Yamauchi; Sho Sonoda; Akiyoshi Sannai; Wataru Kumagai (CoRR, 2023)

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)

Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE. - Koji Hashimoto; Yuji Hirono; Akiyoshi Sannai (CoRR, 2024)

Invariant and Equivariant Reynolds Networks. - Akiyoshi Sannai; Makoto Kawano; Wataru Kumagai (J. Mach. Learn. Res., 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)

Universal approximation with neural networks on function spaces. - Wataru Kumagai; Akiyoshi Sannai; Makoto Kawano (J. Exp. Theor. Artif. Intell., 2024)

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

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)

Prover Agent: An Agent-based Framework for Formal Mathematical Proofs. - Kaito Baba; Chaoran Liu; Shuhei Kurita; Akiyoshi Sannai (CoRR, 2025)

LeanConjecturer: Automatic Generation of Mathematical Conjectures for Theorem Proving. - Naoto Onda; Kazumi Kasaura; Yuta Oriike; Masaya Taniguchi; Akiyoshi Sannai; Sho Sonoda (CoRR, 2025)