Search Results for author: Yoshinobu Kawahara

Found 33 papers, 8 papers with code

Learning interaction rules from multi-animal trajectories via augmented behavioral models

1 code implementation NeurIPS 2021 Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara

In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.

Time Series

Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning

1 code implementation30 Jun 2021 Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham Kakade, Yoshinobu Kawahara

In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics.

online learning reinforcement-learning

A Quadratic Actor Network for Model-Free Reinforcement Learning

1 code implementation11 Mar 2021 Matthias Weissenbacher, Yoshinobu Kawahara

In this work we discuss the incorporation of quadratic neurons into policy networks in the context of model-free actor-critic reinforcement learning.

Continuous Control reinforcement-learning

Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections

no code implementations19 Feb 2021 Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara

Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.

Meta-Learning for Koopman Spectral Analysis with Short Time-series

no code implementations9 Feb 2021 Tomoharu Iwata, Yoshinobu Kawahara

With the proposed method, a representation of a given short time-series is obtained by a bidirectional LSTM for extracting its properties.

Future prediction Meta-Learning +1

Reproducing kernel Hilbert C*-module and kernel mean embeddings

no code implementations27 Jan 2021 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS).

Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics

no code implementations11 Dec 2020 Tomoharu Iwata, Yoshinobu Kawahara

With our proposed method, the forecast error is backpropagated through the neural networks and the spectral decomposition, enabling end-to-end learning of Koopman spectral analysis.

Time Series

Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures

no code implementations29 Jul 2020 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Yoshinobu Kawahara

Kernel mean embedding (KME) is a powerful tool to analyze probability measures for data, where the measures are conventionally embedded into a reproducing kernel Hilbert space (RKHS).

Policy learning with partial observation and mechanical constraints for multi-person modeling

2 code implementations7 Jul 2020 Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields.

Imitation Learning

Learning Dynamics Models with Stable Invariant Sets

no code implementations16 Jun 2020 Naoya Takeishi, Yoshinobu Kawahara

Invariance and stability are essential notions in dynamical systems study, and thus it is of great interest to learn a dynamics model with a stable invariant set.

On Anomaly Interpretation via Shapley Values

1 code implementation9 Apr 2020 Naoya Takeishi, Yoshinobu Kawahara

We focus on the semi-supervised anomaly detection and newly propose a characteristic function, on which the Shapley value is computed, specifically for anomaly scores.

Anomaly Detection Semi-supervised Anomaly Detection

Analysis via Orthonormal Systems in Reproducing Kernel Hilbert $C^*$-Modules and Applications

no code implementations2 Mar 2020 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS).


no code implementations25 Sep 2019 Israr Ul Haq, Yoshinobu Kawahara

Extracting underlying dynamics of objects in image sequences is one of the challenging problems in computer vision.

Time Series Video Classification

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise

no code implementations9 Sep 2019 Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara

In this paper, we address a lifted representation of nonlinear dynamical systems with random noise based on transfer operators, and develop a novel Krylov subspace method for estimating the operators using finite data, with consideration of the unboundedness of operators.

Anomaly Detection

Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces

no code implementations17 Jun 2019 Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara

We empirically illustrate our metric with synthetic data, and evaluate it in the context of the independence test for random processes.

Physically-interpretable classification of biological network dynamics for complex collective motions

1 code implementation13 May 2019 Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara

A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties.

Classification General Classification

An efficient branch-and-cut algorithm for approximately submodular function maximization

no code implementations26 Apr 2019 Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara

For the problem of maximizing an approximately submodular function (ASFM problem), a greedy algorithm quickly finds good feasible solutions for many instances while guaranteeing ($1-e^{-\gamma}$)-approximation ratio for a given submodular ratio $\gamma$.

Knowledge-Based Regularization in Generative Modeling

no code implementations6 Feb 2019 Naoya Takeishi, Yoshinobu Kawahara

Prior domain knowledge can greatly help to learn generative models.

An efficient branch-and-bound algorithm for submodular function maximization

no code implementations10 Nov 2018 Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara

Nemhauser and Wolsey developed an exact algorithm called the constraint generation algorithm that starts from a reduced BIP problem with a small subset of constraints taken from the constraints and repeats solving a reduced BIP problem while adding a new constraint at each iteration.

Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables

1 code implementation30 Aug 2018 Keisuke Fujii, Yoshinobu Kawahara

In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem.

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

no code implementations NeurIPS 2017 Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

Spectral decomposition of the Koopman operator is attracting attention as a tool for the analysis of nonlinear dynamical systems.

Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis

no code implementations NeurIPS 2016 Yoshinobu Kawahara

In this paper, we consider a spectral analysis of the Koopman operator in a reproducing kernel Hilbert space (RKHS).

Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions

no code implementations14 Sep 2015 Yoshinobu Kawahara, Yutaro Yamaguchi

The proximal problem for structured penalties obtained via convex relaxations of submodular functions is known to be equivalent to minimizing separable convex functions over the corresponding submodular polyhedra.

Sparse Learning

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model

no code implementations9 Aug 2014 Shohei Shimizu, Aapo Hyvarinen, Yoshinobu Kawahara

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables.

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM

no code implementations22 Jan 2014 Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.

Causal Discovery

Structured Convex Optimization under Submodular Constraints

no code implementations26 Sep 2013 Kiyohito Nagano, Yoshinobu Kawahara

A number of discrete and continuous optimization problems in machine learning are related to convex minimization problems under submodular constraints.

Weighted Likelihood Policy Search with Model Selection

no code implementations NeurIPS 2012 Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara

Reinforcement learning (RL) methods based on direct policy search (DPS) have been actively discussed to achieve an efficient approach to complicated Markov decision processes (MDPs).

Model Selection reinforcement-learning

Efficient network-guided multi-locus association mapping with graph cuts

no code implementations10 Nov 2012 Chloé-Agathe Azencott, Dominik Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt

We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype, while being connected in an underlying network.

Minimum Average Cost Clustering

no code implementations NeurIPS 2010 Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata

In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions.

Submodularity Cuts and Applications

no code implementations NeurIPS 2009 Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes

Several key problems in machine learning, such as feature selection and active learning, can be formulated as submodular set function maximization.

Active Learning

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