no code implementations • 27 May 2023 • Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
A concept-based classifier can explain the decision process of a deep learning model by human-understandable concepts in image classification problems.
no code implementations • 28 Mar 2023 • Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
In this study, we consider a continuous min--max optimization problem $\min_{x \in \mathbb{X} \max_{y \in \mathbb{Y}}}f(x, y)$ whose objective function is a black-box.
1 code implementation • 31 Jan 2023 • Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents.
no code implementations • 29 Nov 2022 • Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
To reduce the number of simulations required and increase the number of restarts for better local optimum solutions, we propose a new approach referred to as adaptive scenario subset selection (AS3).
1 code implementation • 7 Nov 2022 • Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set.
no code implementations • 26 Sep 2022 • Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
Evolution strategy (ES) is one of promising classes of algorithms for black-box continuous optimization.
1 code implementation • 22 Sep 2022 • Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
Therefore, we propose a loss function that can improve the separation of the important class by setting the margin only for the important class, called Class-sensitive Additive Angular Margin Loss (CAMRI Loss).
no code implementations • 6 Apr 2022 • Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
(I) As the influence of the interaction term between $x$ and $y$ (e. g., $x^\mathrm{T} B y$) on the Lipschitz smooth and strongly convex-concave function $f$ increases, the approaches converge to an optimal solution at a slower rate.
no code implementations • 9 Sep 2021 • Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
The challenge is that we have to discover in an unsupervised manner a set of concepts, i. e., A, B and C, that is useful for the explaining the classifier.
1 code implementation • 13 Apr 2021 • Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels.
no code implementations • 2 Mar 2021 • Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
The convergence rate, that is, the decrease rate of the distance from a search point $m_t$ to the optimal solution $x^*$, is proven to be in $O(\exp( - L / \mathrm{Tr}(H) ))$, where $L$ is the smallest eigenvalue of $H$ and $\mathrm{Tr}(H)$ is the trace of $H$.
no code implementations • 27 May 2019 • Kazuto Fukuchi, Chia-Mu Yu, Arashi Haishima, Jun Sakuma
Instead of considering the worst case, we aim to construct a private mechanism whose error rate is adaptive to the easiness of estimation of the minimum.
2 code implementations • 24 Jan 2019 • Kazuto Fukuchi, Satoshi Hara, Takanori Maehara
The focus of this study is to raise an awareness of the risk of malicious decision-makers who fake fairness by abusing the auditing tools and thereby deceiving the social communities.
no code implementations • 1 Nov 2018 • Jiayang Liu, Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples.
no code implementations • 20 Oct 2017 • Kazuto Fukuchi, Quang Khai Tran, Jun Sakuma
Existing differentially private ERM implicitly assumed that the data contributors submit their private data to a database expecting that the database invokes a differentially private mechanism for publication of the learned model.
no code implementations • ICML 2017 • Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma
This paper develops differentially private mechanisms for $\chi^2$ test of independence.
no code implementations • 6 Nov 2015 • Kazuto Fukuchi, Jun Sakuma
Currently, machine learning plays an important role in the lives and individual activities of numerous people.
no code implementations • 24 Jul 2015 • Rina Okada, Kazuto Fukuchi, Kazuya Kakizaki, Jun Sakuma
One is the query to count outliers, which reports the number of outliers that appear in a given subspace.
no code implementations • 25 Jun 2015 • Kazuto Fukuchi, Jun Sakuma
In this paper, we propose a general framework for fairness-aware learning that uses f-divergences and that covers most of the dependency measures employed in the existing methods.