22 citations · 136 across the 44 of their papers we have counts for
44 papers
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada +1
We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been pro…
Randomized subspace gradient method for constrained optimization
Ryota Nozawa, Pierre-Louis Poirion, Akiko Takeda
We propose randomized subspace gradient methods for high-dimensional constrained optimization. While there have been similarly purposed studies on unconstrained optimization proble…
Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty
Yuya Hikima, Akiko Takeda
Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previo…
Universal heavy-ball method for nonconvex optimization under Hölder continuous Hessians
Naoki Marumo, Akiko Takeda
We propose a new first-order method for minimizing nonconvex functions with Lipschitz continuous gradients and Hölder continuous Hessians. The proposed algorithm is a heavy-ball me…
Parameter-free accelerated gradient descent for nonconvex minimization
Naoki Marumo, Akiko Takeda
We propose a new first-order method for minimizing nonconvex functions with a Lipschitz continuous gradient and Hessian. The proposed method is an accelerated gradient descent with…
Riemannian Levenberg-Marquardt Method with Global and Local Convergence Properties
Sho Adachi, Takayuki Okuno, Akiko Takeda
We extend the Levenberg-Marquardt method on Euclidean spaces to Riemannian manifolds. Although a Riemannian Levenberg-Marquardt (RLM) method was produced by Peeters in 1993, to the…
Theoretical analysis of the randomized subspace regularized Newton method for non-convex optimization
Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
While there already exist randomized subspace Newton methods that restrict the search direction to a random subspace for a convex function, we propose a randomized subspace regular…
Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints
Tianxiang Liu, Ting Kei Pong, Akiko Takeda
We consider a class of sparsity-inducing optimization problems whose constraint set is regularizer-compatible, in the sense that, the constraint set becomes easy-to-project-onto af…
A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis
Issey Sukeda, Atsushi Miyauchi, Akiko Takeda
Community detection is a fundamental network-analysis primitive with a variety of applications in diverse domains. Although the modularity introduced by Newman and Girvan (2004) ha…
Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence
Naoki Marumo, Takayuki Okuno, Akiko Takeda
Minimizing the sum of a convex function and a composite function appears in various fields. The generalized Levenberg--Marquardt (LM) method, also known as the prox-linear method,…
Single Loop Gaussian Homotopy Method for Non-convex Optimization
Hidenori Iwakiri, Yuhang Wang, Shinji Ito +1
The Gaussian homotopy (GH) method is a popular approach to finding better stationary points for non-convex optimization problems by gradually reducing a parameter value , which…
Stable Linear System Identification with Prior Knowledge by Riemannian Sequential Quadratic Optimization
Mitsuaki Obara, Kazuhiro Sato, Hiroki Sakamoto +2
We consider an identification method for a linear continuous time-invariant autonomous system from noisy state observations. In particular, we focus on the identification to satisf…
Unified Smoothing Approach for Best Hyperparameter Selection Problem Using a Bilevel Optimization Strategy
Jan Harold Alcantara, Chieu Thanh Nguyen, Takayuki Okuno +2
Strongly motivated from use in various fields including machine learning, the methodology of sparse optimization has been developed intensively so far. Especially, the recent advan…
A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion
Naoki Marumo, Atsushi Miyauchi, Akiko Takeda +1
Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body…
Convexification with bounded gap for randomly projected quadratic optimization
Terunari Fuji, Pierre-Louis Poirion, Akiko Takeda
Random projection techniques based on Johnson-Lindenstrauss lemma are used for randomly aggregating the constraints or variables of optimization problems while approximately preser…
A Gradient Method for Multilevel Optimization
Ryo Sato, Mirai Tanaka, Akiko Takeda
Although application examples of multilevel optimization have already been discussed since the 1990s, the development of solution methods was almost limited to bilevel cases due to…
BODAME: Bilevel Optimization for Defense Against Model Extraction
Yuto Mori, Atsushi Nitanda, Akiko Takeda
Model extraction attacks have become serious issues for service providers using machine learning. We consider an adversarial setting to prevent model extraction under the assumptio…
Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems
Shun Arahata, Takayuki Okuno, Akiko Takeda
We propose a primal-dual interior-point method (IPM) with convergence to second-order stationary points (SOSPs) of nonlinear semidefinite optimization problems, abbreviated as NSDP…
Primal-dual subgradient method for constrained convex optimization problems
Michael R. Metel, Akiko Takeda
This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simp…
Random projection of Linear and Semidefinite problem with linear inequalities
Pierre-Louis Poirion, Bruno F. Lourenço, Akiko Takeda
The Johnson-Lindenstrauss Lemma states that there exist linear maps that project a set of points of a vector space into a space of much lower dimension such that the Euclidean dist…
Theory and Algorithms for Shapelet-based Multiple-Instance Learning
Daiki Suehiro, Kohei Hatano, Eiji Takimoto +3
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. The goal is to find a good classifier of bags…
Majorization-Minimization-Based Levenberg--Marquardt Method for Constrained Nonlinear Least Squares
Naoki Marumo, Takayuki Okuno, Akiko Takeda
A new Levenberg--Marquardt (LM) method for solving nonlinear least squares problems with convex constraints is described. Various versions of the LM method have been proposed, thei…
Perturbed Iterate SGD for Lipschitz Continuous Loss Functions
Michael R. Metel, Akiko Takeda
This paper presents an extension of stochastic gradient descent for the minimization of Lipschitz continuous loss functions. Our motivation is for use in non-smooth non-convex stoc…
Convex Fairness Constrained Model Using Causal Effect Estimators
Hikaru Ogura, Akiko Takeda
Recent years have seen much research on fairness in machine learning. Here, mean difference (MD) or demographic parity is one of the most popular measures of fairness. However, MD…