| Variational inference based on robust divergences F Futami, I Sato, M Sugiyama International Conference on Artificial Intelligence and Statistics, 813-822, 2018 | 75 | 2018 |
| Bayesian posterior approximation via greedy particle optimization F Futami, Z Cui, I Sato, M Sugiyama Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3606-3613, 2019 | 26 | 2019 |
| Accelerating the diffusion-based ensemble sampling by non-reversible dynamics F Futami, I Sato, M Sugiyama International Conference on Machine Learning, 2020, 2020 | 15 | 2020 |
| Accelerated diffusion-based sampling by the non-reversible dynamics with skew-symmetric matrices F Futami, T Iwata, N Ueda, I Sato Entropy 23 (8), 993, 2021 | 7 | 2021 |
| Expectation propagation for t-exponential family using q-algebra F Futami, I Sato, M Sugiyama In Proceedings of the 31st International Conference on Neural Information …, 2017 | 7 | 2017 |
| Time-independent information-theoretic generalization bounds for SGLD F Futami, M Fujisawa Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Loss function based second-order Jensen inequality and its application to particle variational inference F Futami, T Iwata, I Sato, M Sugiyama Advances in Neural Information Processing Systems 34, 6803-6815, 2021 | 5 | 2021 |
| Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time H Imamura, N Charoenphakdee, F Futami, I Sato, J Honda, M Sugiyama arXiv preprint arXiv:2003.04691, 2020 | 5 | 2020 |
| Excess risk analysis for epistemic uncertainty with application to variational inference F Futami, T Iwata, N Ueda, I Sato, M Sugiyama arXiv preprint arXiv:2206.01606, 2022 | 3 | 2022 |
| Information-theoretic Analysis of Bayesian Test Data Sensitivity F Futami, T Iwata International Conference on Artificial Intelligence and Statistics, 1099-1107, 2024 | 2* | 2024 |
| Predictive variational Bayesian inference as risk-seeking optimization F Futami, T Iwata, N Ueda, I Sato, M Sugiyama International Conference on Artificial Intelligence and Statistics, 5051-5083, 2022 | 2 | 2022 |
| Skew-symmetrically perturbed gradient flow for convex optimization F Futami, T Iwata, N Ueda, I Yamane Asian Conference on Machine Learning, 721-736, 2021 | 1 | 2021 |
| PAC-Bayes Analysis for Recalibration in Classification M Fujisawa, F Futami arXiv preprint arXiv:2406.06227, 2024 | | 2024 |
| Information-theoretic Generalization Analysis for Expected Calibration Error F Futami, M Fujisawa arXiv preprint arXiv:2405.15709, 2024 | | 2024 |
| Scalable gradient matching based on state space Gaussian Processes F Futami Asian Conference on Machine Learning, 769-784, 2021 | | 2021 |
| Expectation Propagation for t-Exponential Family F Futami, I Sato, M Sugiyama IEICE Technical Report; IEICE Tech. Rep. 117 (110), 179-184, 2017 | | 2017 |
| Information-theoretic Generalization Analysis for Vector-Quantized VAEs F Futami, M Fujisawa Workshop on Machine Learning and Compression, NeurIPS 2024, 0 | | |
| Convergence of SVGD in KL divergence via approximate gradient flow M Fujisawa, F Futami | | |
