Search Results for author: Akiko Takeda

Found 15 papers, 4 papers with code

A Gradient Method for Multilevel Optimization

no code implementations28 May 2021 Ryo Sato, Mirai Tanaka, Akiko Takeda

Although application examples of multilevel optimization have already been discussed since the '90s, the development of solution methods was almost limited to bilevel cases due to the difficulty of the problem.

bilevel optimization Data Poisoning

BODAME: Bilevel Optimization for Defense Against Model Extraction

no code implementations11 Mar 2021 Yuto Mori, Atsushi Nitanda, Akiko Takeda

Model extraction attacks have become serious issues for service providers using machine learning.

bilevel optimization Model extraction

Theory and Algorithms for Shapelet-based Multiple-Instance Learning

1 code implementation31 May 2020 Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda

We propose a new formulation of Multiple-Instance Learning (MIL), in which a unit of data consists of a set of instances called a bag.

Multiple Instance Learning Time Series +1

Convex Fairness Constrained Model Using Causal Effect Estimators

no code implementations16 Feb 2020 Hikaru Ogura, Akiko Takeda

However, MD quantifies not only discrimination but also explanatory bias which is the difference of outcomes justified by explanatory features.

Fairness

Nonconvex Optimization for Regression with Fairness Constraints

1 code implementation ICML 2018 Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao

However, a fairness level as a constraint induces a nonconvexity of the feasible region, which disables the use of an off-the-shelf convex optimizer.

Fairness Global Optimization

Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model

1 code implementation15 Jun 2018 Daniel Andrade, Akiko Takeda, Kenji Fukumizu

Even more severe, small insignificant partial correlations due to noise can dramatically change the clustering result when evaluating for example with the Bayesian Information Criteria (BIC).

Model Selection

A refined convergence analysis of pDCA$_e$ with applications to simultaneous sparse recovery and outlier detection

no code implementations19 Apr 2018 Tianxiang Liu, Ting Kei Pong, Akiko Takeda

Moreover, for a large class of loss functions and regularizers, the KL exponent of the corresponding potential function are shown to be 1/2, which implies that the pDCA$_e$ is locally linearly convergent when applied to these problems.

Outlier Detection

Position-based Multiple-play Bandit Problem with Unknown Position Bias

no code implementations NeurIPS 2017 Junpei Komiyama, Junya Honda, Akiko Takeda

Motivated by online advertising, we study a multiple-play multi-armed bandit problem with position bias that involves several slots and the latter slots yield fewer rewards.

Optimistic Robust Optimization With Applications To Machine Learning

no code implementations20 Nov 2017 Matthew Norton, Akiko Takeda, Alexander Mafusalov

In this paper, we explore an optimistic, or best-case view of uncertainty and show that it can be a fruitful approach.

A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems

no code implementations16 Oct 2017 Tianxiang Liu, Ting Kei Pong, Akiko Takeda

We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a smooth function and a finite number of nonnegative proper closed possibly nonsmooth functions (whose proximal mappings are easy to compute), some of which are further composed with linear maps.

Trimmed Density Ratio Estimation

1 code implementation NeurIPS 2017 Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu

Density ratio estimation is a vital tool in both machine learning and statistical community.

Density Ratio Estimation

Breakdown Point of Robust Support Vector Machine

no code implementations3 Sep 2014 Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

For learning parameters such as the regularization parameter in our algorithm, we derive a simple formula that guarantees the robustness of the classifier.

Outlier Detection

Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering

no code implementations NeurIPS 2013 Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi

However, Bayesian learning is often obstructed by computational difficulty: the rigorous Bayesian learning is intractable in many models, and its variational Bayesian (VB) approximation is prone to suffer from local minima.

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