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ORCIDFutoshi Futami
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2020 – today
- 2024
[c10]Futoshi Futami, Tomoharu Iwata:
Information-theoretic Analysis of Bayesian Test Data Sensitivity. AISTATS 2024: 1099-1107- [i8]Futoshi Futami, Masahiro Fujisawa:
Information-theoretic Generalization Analysis for Expected Calibration Error. CoRR abs/2405.15709 (2024) - [i7]Masahiro Fujisawa, Futoshi Futami:
PAC-Bayes Analysis for Recalibration in Classification. CoRR abs/2406.06227 (2024) - 2023
- [c9]Futoshi Futami, Masahiro Fujisawa:
Time-Independent Information-Theoretic Generalization Bounds for SGLD. NeurIPS 2023 - [i6]Futoshi Futami, Tomoharu Iwata:
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty. CoRR abs/2307.12456 (2023) - [i5]Futoshi Futami, Masahiro Fujisawa:
Time-Independent Information-Theoretic Generalization Bounds for SGLD. CoRR abs/2311.01046 (2023) - 2022
- [c8]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS 2022: 5051-5083 - [i4]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR abs/2206.01606 (2022) - 2021
- [j1]Futoshi Futami
, Tomoharu Iwata, Naonori Ueda, Issei Sato:
Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices. Entropy 23(8): 993 (2021) - [c7]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Ikko Yamane:
Skew-symmetrically perturbed gradient flow for convex optimization. ACML 2021: 721-736 - [c6]Futoshi Futami:
Scalable gradient matching based on state space Gaussian Processes. ACML 2021: 769-784 - [c5]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS 2021: 6803-6815 - [i3]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. CoRR abs/2106.05010 (2021) - 2020
- [c4]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. ICML 2020: 3337-3347 - [i2]Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami, Issei Sato, Junya Honda, Masashi Sugiyama:
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. CoRR abs/2003.04691 (2020)
2010 – 2019
- 2019
- [c3]Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Bayesian Posterior Approximation via Greedy Particle Optimization. AAAI 2019: 3606-3613 - 2018
- [c2]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Variational Inference based on Robust Divergences. AISTATS 2018: 813-822 - [i1]Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Frank-Wolfe Stein Sampling. CoRR abs/1805.07912 (2018) - 2017
- [c1]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Expectation Propagation for t-Exponential Family Using q-Algebra. NIPS 2017: 2245-2254
Coauthor Index
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