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05/22/2023
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Modeling of real-world biological multi-agents is a fundamental problem ...
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12/26/2022
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies
We propose a neural network-based model for nonlinear dynamics in contin...
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08/16/2022
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces
We propose a data-driven method for controlling the frequency and conver...
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07/15/2022
Stable Invariant Models via Koopman Spectra
Weight-tied models have attracted attention in the modern development of...
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06/04/2022
Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios
Evaluation of intervention in a multi-agent system, e.g., when humans sh...
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11/02/2021
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Offline reinforcement learning leverages large datasets to train policie...
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07/12/2021
Learning interaction rules from multi-animal trajectories via augmented behavioral models
Extracting the interaction rules of biological agents from moving sequen...
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06/30/2021
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning
Most modern reinforcement learning algorithms optimize a cumulative sing...
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03/11/2021
A Quadratic Actor Network for Model-Free Reinforcement Learning
In this work we discuss the incorporation of quadratic neurons into poli...
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02/19/2021
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Extracting coherent patterns is one of the standard approaches towards u...
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02/09/2021
Meta-Learning for Koopman Spectral Analysis with Short Time-series
Koopman spectral analysis has attracted attention for nonlinear dynamica...
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01/27/2021
Reproducing kernel Hilbert C*-module and kernel mean embeddings
Kernel methods have been among the most popular techniques in machine le...
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12/11/2020
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics
Koopman spectral analysis has attracted attention for understanding nonl...
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07/29/2020
Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures
Kernel mean embedding (KME) is a powerful tool to analyze probability me...
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07/07/2020
Policy learning with partial observation and mechanical constraints for multi-person modeling
Extracting the rules of real-world biological multi-agent behaviors is a...
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06/16/2020
Learning Dynamics Models with Stable Invariant Sets
Stable invariant sets are an essential notion in the analysis and applic...
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04/09/2020
On Anomaly Interpretation via Shapley Values
Anomaly localization is an essential problem as anomaly detection is. Be...
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03/02/2020
Analysis via Orthonormal Systems in Reproducing Kernel Hilbert C^*-Modules and Applications
Kernel methods have been among the most popular techniques in machine le...
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09/09/2019
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise
Operator-theoretic analysis of nonlinear dynamical systems has attracted...
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06/17/2019
Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces
The development of a metric on structural data-generating mechanisms is ...
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05/13/2019
Physically-interpretable classification of network dynamics for complex collective motions
Understanding complex network dynamics is a fundamental issue in various...
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04/26/2019
An efficient branch-and-cut algorithm for approximately submodular function maximization
When approaching to problems in computer science, we often encounter sit...
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02/06/2019
Regularizing Generative Models Using Knowledge of Feature Dependence
Generative modeling is a fundamental problem in machine learning with ma...
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11/10/2018
An efficient branch-and-bound algorithm for submodular function maximization
The submodular function maximization is an attractive optimization model...
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08/30/2018
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
Understanding nonlinear dynamical systems (NLDSs) is challenging in a va...
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05/31/2018
Metric on Nonlinear Dynamical Systems with Koopman Operators
The development of a metric for structural data is a long-term problem i...
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10/12/2017
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Spectral decomposition of the Koopman operator is attracting attention a...
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08/09/2014
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Structural equation models and Bayesian networks have been widely used t...
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01/22/2014
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Discovering causal relations among observed variables in a given data se...
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09/26/2013
Structured Convex Optimization under Submodular Constraints
A number of discrete and continuous optimization problems in machine lea...
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11/10/2012
Efficient network-guided multi-locus association mapping with graph cuts
As an increasing number of genome-wide association studies reveal the li...
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02/14/2012
Discovering causal structures in binary exclusive-or skew acyclic models
Discovering causal relations among observed variables in a given data se...
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01/13/2011