88 citations · 148 across the 18 of their papers we have counts for
12 papers · 1 filter
Loss function based second-order Jensen inequality and its application to particle variational inference
Futoshi Futami, Tomoharu Iwata, Naonori Ueda +2
Bayesian model averaging, obtained as the expectation of a likelihood function by a posterior distribution, has been widely used for prediction, evaluation of uncertainty, and mode…
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain
Takahiro Mimori, Keiko Sasada, Hirotaka Matsui +1
We propose an evaluation framework for class probability estimates (CPEs) in the presence of label uncertainty, which is commonly observed as diagnosis disagreement between experts…
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami +3
The Gaussian process bandit is a problem in which we want to find a maximizer of a black-box function with the minimum number of function evaluations. If the black-box function var…
Bayesian interpretation of SGD as Ito process
Soma Yokoi, Issei Sato
The current interpretation of stochastic gradient descent (SGD) as a stochastic process lacks generality in that its numerical scheme restricts continuous-time dynamics as well as…
On Transformations in Stochastic Gradient MCMC
Soma Yokoi, Takuma Otsuka, Issei Sato
Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbo…
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
The notion of flat minima has played a key role in the generalization studies of deep learning models. However, existing definitions of the flatness are known to be sensitive to th…